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Journal articles on the topic 'Text summarisation'

Author: Grafiati

Published: 4 June 2021

Last updated: 14 February 2022

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1

Suleiman, Dima, and Arafat Awajan. "Deep Learning Based Abstractive Text Summarization: Approaches, Datasets, Evaluation Measures, and Challenges." Mathematical Problems in Engineering 2020 (August 24, 2020): 1–29. http://dx.doi.org/10.1155/2020/9365340.

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In recent years, the volume of textual data has rapidly increased, which has generated a valuable resource for extracting and analysing information. To retrieve useful knowledge within a reasonable time period, this information must be summarised. This paper reviews recent approaches for abstractive text summarisation using deep learning models. In addition, existing datasets for training and validating these approaches are reviewed, and their features and limitations are presented. The Gigaword dataset is commonly employed for single-sentence summary approaches, while the Cable News Network (CNN)/Daily Mail dataset is commonly employed for multisentence summary approaches. Furthermore, the measures that are utilised to evaluate the quality of summarisation are investigated, and Recall-Oriented Understudy for Gisting Evaluation 1 (ROUGE1), ROUGE2, and ROUGE-L are determined to be the most commonly applied metrics. The challenges that are encountered during the summarisation process and the solutions proposed in each approach are analysed. The analysis of the several approaches shows that recurrent neural networks with an attention mechanism and long short-term memory (LSTM) are the most prevalent techniques for abstractive text summarisation. The experimental results show that text summarisation with a pretrained encoder model achieved the highest values for ROUGE1, ROUGE2, and ROUGE-L (43.85, 20.34, and 39.9, respectively). Furthermore, it was determined that most abstractive text summarisation models faced challenges such as the unavailability of a golden token at testing time, out-of-vocabulary (OOV) words, summary sentence repetition, inaccurate sentences, and fake facts.

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2

Reeve, Lawrence H., Hyoil Han, and Ari D. Brooks. "Biomedical text summarisation using concept chains." International Journal of Data Mining and Bioinformatics 1, no. 4 (2007): 389. http://dx.doi.org/10.1504/ijdmb.2007.012967.

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3

Sartakhti, Moein Salimi, Ahmad Yoosofan, Ali Asghar Fatehi, and Ali Rahimi. "Single Document Summarization Based on Grey Wolf Optimization." Global Journal of Computer Sciences: Theory and Research 10, no. 2 (October 30, 2020): 48–56. http://dx.doi.org/10.18844/gjcs.v10i2.5807.

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The amazing growth of online services has caused an information explosion issue. Text summarisation is condensing the text into a small version and preserving its overall concept. Text summarisation is an important way to extract significant information from documents and offer that information to the user in an abbreviated form while preserving its major content. For human beings, it is very difficult to summarise large documents. To do this, this paper uses some sentence features and word features. These features assign scores to all the sentences. In this paper, we combine these features by Grey Wolf Optimiser (GWO). Optimisation of features gives better results than using individual features. This is the first attempt to show the performance of GWO for Persian text summarisation. The proposed method is compared with the genetic algorithm and the evolutionary strategy. The results show that our model will be useful in this research area. Keywords: Text summarisation, genetic algorithm, sentence, score function, evolutionary strategy.

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4

Lloret, Elena, and Manuel Palomar. "Text summarisation in progress: a literature review." Artificial Intelligence Review 37, no. 1 (April 30, 2011): 1–41. http://dx.doi.org/10.1007/s10462-011-9216-z.

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5

Jayashree, R., K. Srikanta Murthy, Basavaraj S. Anami, and Alex Pappachen James. "The impact of feature selection on text summarisation." International Journal of Applied Pattern Recognition 1, no. 4 (2014): 377. http://dx.doi.org/10.1504/ijapr.2014.068344.

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Zerva, Chrysoula, Minh-Quoc Nghiem, Nhung T. H. Nguyen, and Sophia Ananiadou. "Cited text span identification for scientific summarisation using pre-trained encoders." Scientometrics 125, no. 3 (May 7, 2020): 3109–37. http://dx.doi.org/10.1007/s11192-020-03455-z.

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AbstractWe present our approach for the identification of cited text spans in scientific literature, using pre-trained encoders (BERT) in combination with different neural networks. We further experiment to assess the impact of using these cited text spans as input in BERT-based extractive summarisation methods. Inspired and motivated by the CL-SciSumm shared tasks, we explore different methods to adapt pre-trained models which are tuned for generic domain to scientific literature. For the identification of cited text spans, we assess the impact of different configurations in terms of learning from augmented data and using different features and network architectures (BERT, XLNET, CNN, and BiMPM) for training. We show that identifying and fine-tuning the language models on unlabelled or augmented domain specific data can improve the performance of cited text span identification models. For the scientific summarisation we implement an extractive summarisation model adapted from BERT. With respect to the input sentences taken from the cited paper, we explore two different scenarios: (1) consider all the sentences (full-text) of the referenced article as input and (2) consider only the text spans that have been identified to be cited by other publications. We observe that in certain experiments, by using only the cited text-spans we can achieve better performance, while minimising the input size needed.

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7

LLORET, ELENA, and MANUEL PALOMAR. "COMPENDIUM: a text summarisation tool for generating summaries of multiple purposes, domains, and genres." Natural Language Engineering 19, no. 2 (July 16, 2012): 147–86. http://dx.doi.org/10.1017/s1351324912000198.

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AbstractIn this paper, we present a Text Summarisation tool, compendium, capable of generating the most common types of summaries. Regarding the input, single- and multi-document summaries can be produced; as the output, the summaries can be extractive or abstractive-oriented; and finally, concerning their purpose, the summaries can be generic, query-focused, or sentiment-based. The proposed architecture for compendium is divided in various stages, making a distinction between core and additional stages. The former constitute the backbone of the tool and are common for the generation of any type of summary, whereas the latter are used for enhancing the capabilities of the tool. The main contributions of compendium with respect to the state-of-the-art summarisation systems are that (i) it specifically deals with the problem of redundancy, by means of textual entailment; (ii) it combines statistical and cognitive-based techniques for determining relevant content; and (iii) it proposes an abstractive-oriented approach for facing the challenge of abstractive summarisation. The evaluation performed in different domains and textual genres, comprising traditional texts, as well as texts extracted from the Web 2.0, shows that compendium is very competitive and appropriate to be used as a tool for generating summaries.

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8

Hariharan, Shanmugasundaram, and Rengaramanujam Srinivasan. "A Comparison of Similarity Measures for Text Documents." Journal of Information & Knowledge Management 07, no. 01 (March 2008): 1–8. http://dx.doi.org/10.1142/s0219649208001889.

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Similarity is an important and widely used concept in many applications such as Document Summarisation, Question Answering, Information Retrieval, Document Clustering and Categorisation. This paper presents a comparison of various similarity measures in comparing the content of text documents. We have attempted to find the best measure suited for finding the document similarity for newspaper reports.

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9

Orăsan, Constantin. "Automatic summarisation: 25 years On." Natural Language Engineering 25, no. 06 (September 19, 2019): 735–51. http://dx.doi.org/10.1017/s1351324919000524.

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AbstractAutomatic text summarisation is a topic that has been receiving attention from the research community from the early days of computational linguistics, but it really took off around 25 years ago. This article presents the main developments from the last 25 years. It starts by defining what a summary is and how its definition changed over time as a result of the interest in processing new types of documents. The article continues with a brief history of the field and highlights the main challenges posed by the evaluation of summaries. The article finishes with some thoughts about the future of the field.

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10

Jayashree, R., K. Srikanta Murthy, and Basavaraj S. Anami. "Hybrid methodologies for summarisation of Kannada language text documents." International Journal of Knowledge Engineering and Data Mining 3, no. 1 (2014): 82. http://dx.doi.org/10.1504/ijkedm.2014.066238.

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Hannah, M. Esther, and Saswati Mukherjee. "A classification-based summarisation model for summarising text documents." International Journal of Information and Communication Technology 6, no. 3/4 (2014): 292. http://dx.doi.org/10.1504/ijict.2014.063217.

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12

Gupta, Anand, and Manpreet Kaur. "Text Summarisation Using Laplacian Centrality-Based Minimum Vertex Cover." Journal of Information & Knowledge Management 18, no. 04 (December 2019): 1950050. http://dx.doi.org/10.1142/s0219649219500503.

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Outdegree Centrality (OC) is a graph-based centrality measure that captures local connectedness of a node in a graph. The measure has been used in the literature to highlight key sentences in a graph-based optimisation method for summarisation. It is observed in resultant summaries that OC tends to be biased towards selecting introductory sentences of the document producing only generic summaries. The different graph centrality measures lead to different interpretations of a summary. Therefore, the authors propose to use another suitable centrality measure in order to generate more specific summary rather than a generic summary. Such a summary is expected to be highly informative covering all the subtopics of the source document. This requirement has instigated the authors to use Laplacian Centrality (LC) measure to find the significance of the nodes. The essence of this measure lies in highlighting central nodes from subgraphs which contribute non-uniformly towards the common goal of the graph. The modified method has shown significant improvement in informativeness and coherence of summaries and outperformed state-of-the-art results.

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13

Padmapriya, G., and K. Duraiswamy. "Multi-document-based text summarisation through deep learning algorithm." International Journal of Business Intelligence and Data Mining 16, no. 4 (2020): 459. http://dx.doi.org/10.1504/ijbidm.2020.107546.

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14

Zaman, Farooq, Matthew Shardlow, Saeed-Ul Hassan, Naif Radi Aljohani, and Raheel Nawaz. "HTSS: A novel hybrid text summarisation and simplification architecture." Information Processing & Management 57, no. 6 (November 2020): 102351. http://dx.doi.org/10.1016/j.ipm.2020.102351.

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15

Shaguna Awasth. "Implementing Supervised Approach to Summarization of Research Papers." International Journal for Modern Trends in Science and Technology 6, no. 12 (December 18, 2020): 398–401. http://dx.doi.org/10.46501/ijmtst061275.

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Using automatic text summarization we can reduce a document to its main information or to what is known as crux of the document .Recent research in this zone has zeroed in on neural ways to deal with summarisation, which can be very data hungry. This paper aims to explore a quicker way by implementing a supervised-learning based extractive summarisation system for the summarisation of research papers. This paper also explores the possibility of any section, in a research paper being the prime section to generate summaries by utilizing ROUGE scores. An easy to implement and intuitive model is developed using glove embeddings and doc2vec to encode sentences and documents in their local and global context producing grammatically coherent summaries.

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16

Dahl, Trine. "Text Summarisation: From Human Activity to Computer Program. The Problem of Tacit Knowledge." HERMES - Journal of Language and Communication in Business 13, no. 25 (February 23, 2017): 113. http://dx.doi.org/10.7146/hjlcb.v13i25.25588.

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In this article I discuss whether the human activity of text summarisation can be successfully simulated in a computer. In order to write a computer program that produces high-quality summaries it becomes necessary to specify the cognitive pro-cesses involved when humans summarise text. As texts can be summarised in many different ways, evaluation of summaries becomes an important aspect in the discussion. The article discusses relevant factors in such an evaluation process. It turns out that humans when summarising texts make use of knowledge which is not readily open to scrutiny; it is tacit knowledge. This makes it very difficult to produce computer-generated summaries which are as good as those produced by skilled humans. New developments within artificial intelligence, relying on network processing techniques, may offer solutions to the problem of dealing with tacit knowledge. At present, accept-able computer summaries may be generated by programs combining accessible human knowledge of the summarisation process and knowledge about text.

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17

Patel, Darshna, and Hitesh Chhinkaniwala. "Testing Methodologies and Exploring Challenges and Issues in Text Summarisation." International Journal of Data Mining And Emerging Technologies 5, no. 2 (2015): 73. http://dx.doi.org/10.5958/2249-3220.2015.00011.7.

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18

Hou, Jiang-Liang, and Yong-Jhih Chen. "A conceptual model for text summarisation based on reader requirements." Journal of Experimental & Theoretical Artificial Intelligence 27, no. 3 (September 11, 2014): 317–23. http://dx.doi.org/10.1080/0952813x.2014.954275.

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19

Almazaydeh, Laiali. "Automatic Arabic text summarisation system (AATSS) based on morphological analysis." International Journal of Intelligent Systems Technologies and Applications 17, no. 3 (2018): 272. http://dx.doi.org/10.1504/ijista.2018.094007.

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20

Almazaydeh, Laiali. "Automatic Arabic text summarisation system (AATSS) based on morphological analysis." International Journal of Intelligent Systems Technologies and Applications 17, no. 3 (2018): 272. http://dx.doi.org/10.1504/ijista.2018.10015230.

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21

Moen, Hans, Laura-Maria Peltonen, Juho Heimonen, Antti Airola, Tapio Pahikkala, Tapio Salakoski, and Sanna Salanterä. "Comparison of automatic summarisation methods for clinical free text notes." Artificial Intelligence in Medicine 67 (February 2016): 25–37. http://dx.doi.org/10.1016/j.artmed.2016.01.003.

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22

SCHILDER, FRANK. "Robust discourse parsing via discourse markers, topicality and position." Natural Language Engineering 8, no. 2-3 (June 2002): 235–55. http://dx.doi.org/10.1017/s1351324902002905.

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This paper describes a simple discourse parsing and analysis algorithm that combines a formal underspecification utilising discourse grammar with Information Retrieval (IR) techniques. First, linguistic knowledge based on discourse markers is used to constrain a totally underspecified discourse representation. Then, the remaining underspecification is further specified by the computation of a topicality score for every discourse unit. This computation is done via the vector space model. Finally, the sentences in a prominent position (e.g. the first sentence of a paragraph) are given an adjusted topicality score. The proposed algorithm was evaluated by applying it to a text summarisation task. Results from a psycholinguistic experiment, indicating the most salient sentences for a given text as the ‘gold standard’, show that the algorithm performs better than commonly used machine learning and statistical approaches to summarisation.

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23

Lagrini, Samira, Nabiha Azizi, Monther Al Dwairi, and Mohammed Redjimi. "Toward an automatic summarisation of Arabic text depending on rhetorical relations." International Journal of Reasoning-based Intelligent Systems 11, no. 3 (2019): 203. http://dx.doi.org/10.1504/ijris.2019.10023432.

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Lagrini, Samira, Nabiha Azizi, Mohammed Redjimi, and Monther Al Dwairi. "Toward an automatic summarisation of Arabic text depending on rhetorical relations." International Journal of Reasoning-based Intelligent Systems 11, no. 3 (2019): 203. http://dx.doi.org/10.1504/ijris.2019.102533.

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Chang, Te Min, and Wen Feng Hsiao. "A K-mixture connective-strength-based approach to automatic text summarisation." International Journal of Intelligent Systems Technologies and Applications 10, no. 2 (2011): 111. http://dx.doi.org/10.1504/ijista.2011.039014.

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26

Sheng, Jack, Yewen Ding, Feihong Zhang, TianXiao Jiang, Ke Jiang, and Wei Fang. "A method of automatic text summarisation based on long short-term memory." International Journal of Computational Science and Engineering 22, no. 1 (2020): 39. http://dx.doi.org/10.1504/ijcse.2020.10029209.

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Fang, Wei, TianXiao Jiang, Ke Jiang, Feihong Zhang, Yewen Ding, and Jack Sheng. "A method of automatic text summarisation based on long short-term memory." International Journal of Computational Science and Engineering 22, no. 1 (2020): 39. http://dx.doi.org/10.1504/ijcse.2020.107243.

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28

Hidouci, Walid Khaled, Abdelkrime Aries, and Djamel Eddine Zegour. "Graph-based cumulative score using statistical features for multilingual automatic text summarisation." International Journal of Data Mining, Modelling and Management 13, no. 1/2 (2021): 37. http://dx.doi.org/10.1504/ijdmmm.2021.10035066.

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Aries, Abdelkrime, Djamel Eddine Zegour, and Walid Khaled Hidouci. "Graph-based cumulative score using statistical features for multilingual automatic text summarisation." International Journal of Data Mining, Modelling and Management 13, no. 1/2 (2021): 37. http://dx.doi.org/10.1504/ijdmmm.2021.112909.

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30

Siddharthan, Advaith. "A survey of research on text simplification." Recent Advances in Automatic Readability Assessment and Text Simplification 165, no. 2 (December 31, 2014): 259–98. http://dx.doi.org/10.1075/itl.165.2.06sid.

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Text simplification, defined narrowly, is the process of reducing the linguistic complexity of a text, while still retaining the original information and meaning. More broadly, text simplification encompasses other operations; for example, conceptual simplification to simplify content as well as form, elaborative modification, where redundancy and explicitness are used to emphasise key points, and text summarisation to omit peripheral or inappropriate information. There is substantial evidence that manual text simplification is an effective intervention for many readers, but automatic simplification has only recently become an established research field. There have been several recent papers on the topic, however, which bring to the table a multitude of methodologies, each with their strengths and weaknesses. The goal of this paper is to summarise the large interdisciplinary body of work on text simplification and highlight the most promising research directions to move the field forward.

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31

Tapas, Guha, and N. Mehala. "Latent semantic analysis in automatic text summarisation: a state-of-the-art analysis." International Journal of Intelligence and Sustainable Computing 1, no. 2 (2021): 128. http://dx.doi.org/10.1504/ijisc.2021.113294.

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32

Shafiee, Fatemeh, and Mehrnoush Shamsfard. "Similarity versus relatedness: A novel approach in extractive Persian document summarisation." Journal of Information Science 44, no. 3 (February 1, 2017): 314–30. http://dx.doi.org/10.1177/0165551517693537.

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Automatic text summarisation is the process of creating a summary from one or more documents by eliminating the details and preserving the worthwhile information. This article presents a single/multi-document summariser using a novel clustering method for creating summaries. First, a feature selection phase is employed. Then, FarsNet, the Persian WordNet, is utilised to extract the semantic information of words. Therefore, the input sentences are categorised into three main clusters: similarity, relatedness and coherency. Each similarity cluster contains similar sentences to its core, while each relatedness cluster contains sentences that are related (but not similar) to its core. The coherency clusters show the sentences that should be kept together to preserve the coherency of the summary. Finally, the centroid of each similarity cluster having the most feature score is added to an empty summary. The summary is enlarged by including related sentences from relatedness clusters and excluding similar sentences to its content iteratively. Coherency clusters are applied to the created summary in the last step. The proposed method has been compared with three known existing text summarisation systems and techniques for the Persian language: FarsiSum, Parsumist and Ijaz. Our proposed method leads to improvement in experimental results on different measurements including precision, recall, F-measure, ROUGE-N and ROUGE-L.

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33

Aghaunor, C. T., and G. O. Ekuobase. "Automatic text summarisation of case law using gate with annie and summa plug-ins." Nigerian Journal of Technology 38, no. 4 (December 12, 2019): 987. http://dx.doi.org/10.4314/njt.v38i4.23.

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Anitha, J., P. V. G. D. Prasad Reddy, and M. S. Prasad Babu. "Error tolerant global search incorporated with deep learning algorithm to automatic Hindi text summarisation." International Journal of Business Intelligence and Data Mining 14, no. 3 (2019): 359. http://dx.doi.org/10.1504/ijbidm.2019.098841.

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35

El-Halees, Alaa, and Ahmed Al-Asmar. "Ontology Based Arabic Opinion Mining." Journal of Information & Knowledge Management 16, no. 03 (July 31, 2017): 1750028. http://dx.doi.org/10.1142/s0219649217500289.

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In Arabic language, studies in the area of opinion mining are still limited compared to that being carried out in other languages. In this paper, we highlight the problem for Arabic opinion mining techniques when analysing reviews having different features with different opinion strengths. The traditional works of opinion mining consider all features extracted from the reviews to be equally important, so they fail to determine the correct opinion of the review and make the review's sentiment classification less accurate. This research presents a technique based on an ontology that uses feature level classification to classify Arabic user-generated reviews by identifying the relevant features from the review based on the degree of these features in the ontology tree. Then, we exploit the important features extracted to determine the overall polarity of the review. Moreover, summarisation for each feature is done to determine which feature has satisfied or dissatisfied customers. To evaluate our work, we use public datasets which are hotels and books datasets. We used [Formula: see text]-measure metrics to assess the performance and compare the results with other supervised and unsupervised techniques. Also, subjective evaluation is used in our method to demonstrate the effectiveness of feature and opinion extraction process and summarisation. We show that our method improves the performance compared with other opinion mining classification approaches, obtaining 78.83% [Formula: see text]-measure in hotels domain and 79.18% in books domain. Furthermore, the subjective evaluation shows the effectiveness of our method by getting an average [Formula: see text]-measure of 84.62% in hotels dataset and 86.31% in books dataset.

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36

Scott, Donia, Catalina Hallett, and Rachel Fettiplace. "Data-to-text summarisation of patient records: Using computer-generated summaries to access patient histories." Patient Education and Counseling 92, no. 2 (August 2013): 153–59. http://dx.doi.org/10.1016/j.pec.2013.04.019.

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37

Osman, Akram, and Naomie Salim. "Extracting useful reply-posts for text forum threads summarisation using quality features and classification methods." International Journal of Data Mining, Modelling and Management 12, no. 3 (2020): 330. http://dx.doi.org/10.1504/ijdmmm.2020.10030685.

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Osman, Akram, and Naomie Salim. "Extracting useful reply-posts for text forum threads summarisation using quality features and classification methods." International Journal of Data Mining, Modelling and Management 12, no. 3 (2020): 330. http://dx.doi.org/10.1504/ijdmmm.2020.108725.

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Hannah, M. Esther. "A hybrid classification-based model for automatic text summarisation using machine learning approaches: CBS-ID3MV." International Journal of Product Development 23, no. 2/3 (2019): 201. http://dx.doi.org/10.1504/ijpd.2019.099242.

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Hannah, M. Esther. "A hybrid classification-based model for automatic text summarisation using machine learning approaches: CBS-ID3MV." International Journal of Product Development 23, no. 2/3 (2019): 201. http://dx.doi.org/10.1504/ijpd.2019.10020397.

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41

Onan, Aytug, Hasan Bulut, and Serdar Korukoglu. "An improved ant algorithm with LDA-based representation for text document clustering." Journal of Information Science 43, no. 2 (March 1, 2016): 275–92. http://dx.doi.org/10.1177/0165551516638784.

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Document clustering can be applied in document organisation and browsing, document summarisation and classification. The identification of an appropriate representation for textual documents is extremely important for the performance of clustering or classification algorithms. Textual documents suffer from the high dimensionality and irrelevancy of text features. Besides, conventional clustering algorithms suffer from several shortcomings, such as slow convergence and sensitivity to the initial value. To tackle the problems of conventional clustering algorithms, metaheuristic algorithms are frequently applied to clustering. In this paper, an improved ant clustering algorithm is presented, where two novel heuristic methods are proposed to enhance the clustering quality of ant-based clustering. In addition, the latent Dirichlet allocation (LDA) is used to represent textual documents in a compact and efficient way. The clustering quality of the proposed ant clustering algorithm is compared to the conventional clustering algorithms using 25 text benchmarks in terms of F-measure values. The experimental results indicate that the proposed clustering scheme outperforms the compared conventional and metaheuristic clustering methods for textual documents.

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42

Alian, Marwah, and Arafat Awajan. "Semantic Similarity for English and Arabic Texts: A Review." Journal of Information & Knowledge Management 19, no. 04 (December 2020): 2050033. http://dx.doi.org/10.1142/s0219649220500331.

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Semantic similarity is the task of measuring relations between sentences or words to determine the degree of similarity or resemblance. Several applications of natural language processing require semantic similarity measurement to achieve good results; these applications include plagiarism detection, text entailment, text summarisation, paraphrasing identification, and information extraction. Many researchers have proposed new methods to measure the semantic similarity of Arabic and English texts. In this research, these methods are reviewed and compared. Results show that the precision of the corpus-based approach exceeds 0.70. The precision of the descriptive feature-based technique is between 0.670 and 0.86, with a Pearson correlation coefficient of over 0.70. Meanwhile, the word embedding technique has a correlation of 0.67, and its accuracy is in the range 0.76–0.80. The best results are achieved by the feature-based approach.

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Lim, JongYoon, Inkyu Sa, Ho Seok Ahn, Norina Gasteiger, Sanghyub John Lee, and Bruce MacDonald. "Subsentence Extraction from Text Using Coverage-Based Deep Learning Language Models." Sensors 21, no. 8 (April 12, 2021): 2712. http://dx.doi.org/10.3390/s21082712.

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Sentiment prediction remains a challenging and unresolved task in various research fields, including psychology, neuroscience, and computer science. This stems from its high degree of subjectivity and limited input sources that can effectively capture the actual sentiment. This can be even more challenging with only text-based input. Meanwhile, the rise of deep learning and an unprecedented large volume of data have paved the way for artificial intelligence to perform impressively accurate predictions or even human-level reasoning. Drawing inspiration from this, we propose a coverage-based sentiment and subsentence extraction system that estimates a span of input text and recursively feeds this information back to the networks. The predicted subsentence consists of auxiliary information expressing a sentiment. This is an important building block for enabling vivid and epic sentiment delivery (within the scope of this paper) and for other natural language processing tasks such as text summarisation and Q&A. Our approach outperforms the state-of-the-art approaches by a large margin in subsentence prediction (i.e., Average Jaccard scores from 0.72 to 0.89). For the evaluation, we designed rigorous experiments consisting of 24 ablation studies. Finally, our learned lessons are returned to the community by sharing software packages and a public dataset that can reproduce the results presented in this paper.

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44

Kigotho, Mutuota, and Siti Sarah Fitriani. "Summarising an Explanation Text with a Visual Representation as the Guidelines: How Does this Work to Represent Meaning?" Al-Ta lim Journal 25, no. 1 (March 1, 2018): 1–12. http://dx.doi.org/10.15548/jt.v25i1.379.

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Metacognition has been popular in reading area, especially when it is related to comprehension and the representation of meanings. Combining metacognitive strategies to represent meanings from a text has been done by previous scholars to help readers construct meaning. In this paper, we present students’ drawings and writings as the results of successive visualisation and summarisation activities in the classroom. We intended to find out the extent to which students’ visual representations can be the guideline for them to write summaries. By employing qualitative research method, we collected visual representations and summaries from 26 undergraduate students studying at the English Education Department of Syiah Kuala University. To understand students’ drawings, we consulted some literature on visual literacy and multimodality; while for the analysis of students’ writings, we reviewed some literature on functional model to language. Based on the analysis, a productive visual representation leads to a strong summary, and vice versa. This result is further discussed in this paper.

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Krzywicki, Alfred, Wayne Wobcke, Michael Bain, John Calvo Martinez, and Paul Compton. "Data mining for building knowledge bases: techniques, architectures and applications." Knowledge Engineering Review 31, no. 2 (March 2016): 97–123. http://dx.doi.org/10.1017/s0269888916000047.

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AbstractData mining techniques for extracting knowledge from text have been applied extensively to applications including question answering, document summarisation, event extraction and trend monitoring. However, current methods have mainly been tested on small-scale customised data sets for specific purposes. The availability of large volumes of data and high-velocity data streams (such as social media feeds) motivates the need to automatically extract knowledge from such data sources and to generalise existing approaches to more practical applications. Recently, several architectures have been proposed for what we callknowledge mining: integrating data mining for knowledge extraction from unstructured text (possibly making use of a knowledge base), and at the same time, consistently incorporating this new information into the knowledge base. After describing a number of existing knowledge mining systems, we review the state-of-the-art literature on both current text mining methods (emphasising stream mining) and techniques for the construction and maintenance of knowledge bases. In particular, we focus on mining entities and relations from unstructured text data sources, entity disambiguation, entity linking and question answering. We conclude by highlighting general trends in knowledge mining research and identifying problems that require further research to enable more extensive use of knowledge bases.

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Cardoso, Paula C. F., Thiago A. S. Pardo, and Maite Taboada. "Subtopic annotation and automatic segmentation for news texts in Brazilian Portuguese." Corpora 12, no. 1 (April 2017): 23–54. http://dx.doi.org/10.3366/cor.2017.0108.

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Subtopic segmentation aims to break documents into subtopical text passages, which develop a main topic in a text. Being capable of automatically detecting subtopics is very useful for several Natural Language Processing applications. For instance, in automatic summarisation, having the subtopics at hand enables the production of summaries with good subtopic coverage. Given the usefulness of subtopic segmentation, it is common to assemble a reference-annotated corpus that supports the study of the envisioned phenomena and the development and evaluation of systems. In this paper, we describe the subtopic annotation process in a corpus of news texts written in Brazilian Portuguese, following a systematic annotation process and answering the main research questions when performing corpus annotation. Based on this corpus, we propose novel methods for subtopic segmentation following patterns of discourse organisation, specifically using Rhetorical Structure Theory. We show that discourse structures mirror the subtopic changes in news texts. An important outcome of this work is the freely available annotated corpus, which, to the best of our knowledge, is the only one for Portuguese. We demonstrate that some discourse knowledge may significantly help to find boundaries automatically in a text. In particular, the relation type and the level of the tree structure are important features.

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Ur-Rahman, Nadeem. "Textual Data Mining For Knowledge Discovery and Data Classification: A Comparative Study." European Scientific Journal, ESJ 13, no. 21 (July 31, 2017): 429. http://dx.doi.org/10.19044/esj.2017.v13n21p429.

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Business Intelligence solutions are key to enable industrial organisations (either manufacturing or construction) to remain competitive in the market. These solutions are achieved through analysis of data which is collected, retrieved and re-used for prediction and classification purposes. However many sources of industrial data are not being fully utilised to improve the business processes of the associated industry. It is generally left to the decision makers or managers within a company to take effective decisions based on the information available throughout product design and manufacture or from the operation of business or production processes. Substantial efforts and energy are required in terms of time and money to identify and exploit the appropriate information that is available from the data. Data Mining techniques have long been applied mainly to numerical forms of data available from various data sources but their applications to analyse semi-structured or unstructured databases are still limited to a few specific domains. The applications of these techniques in combination with Text Mining methods based on statistical, natural language processing and visualisation techniques could give beneficial results. Text Mining methods mainly deal with document clustering, text summarisation and classification and mainly rely on methods and techniques available in the area of Information Retrieval (IR). These help to uncover the hidden information in text documents at an initial level. This paper investigates applications of Text Mining in terms of Textual Data Mining (TDM) methods which share techniques from IR and data mining. These techniques may be implemented to analyse textual databases in general but they are demonstrated here using examples of Post Project Reviews (PPR) from the construction industry as a case study. The research is focused on finding key single or multiple term phrases for classifying the documents into two classes i.e. good information and bad information documents to help decision makers or project managers to identify key issues discussed in PPRs which can be used as a guide for future project management process.

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Saad, Marcelo, and Roberta de Medeiros. "Spirituality and Healthcare—Common Grounds for the Secular and Religious Worlds and Its Clinical Implications." Religions 12, no. 1 (December 29, 2020): 22. http://dx.doi.org/10.3390/rel12010022.

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The spiritual dimension of patients has progressively gained more relevance in healthcare in the last decades. However, the term “spiritual” is an open, fluid concept and, for health purposes, no definition of spirituality is universally accepted. Health professionals and researchers have the challenge to cover the entire spectrum of the spiritual level in their practice. This is particularly difficult because most healthcare courses do not prepare their graduates in this field. They also need to face acts of prejudice by their peers or their managers. Here, the authors aim to clarify some common grounds between secular and religious worlds in the realm of spirituality and healthcare. This is a conceptual manuscript based on the available scientific literature and on the authors’ experience. The text explores the secular and religious intersection involving spirituality and healthcare, together with the common ground shared by the two fields, and consequent clinical implications. Summarisations presented here can be a didactic beginning for practitioners or scholars involved in health or behavioural sciences. The authors think this construct can favour accepting the patient’s spiritual dimension importance by healthcare professionals, treatment institutes, and government policies.

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Lal, Tarsem. "Impact of financial inclusion on poverty alleviation through cooperative banks." International Journal of Social Economics 45, no. 5 (May 14, 2018): 808–28. http://dx.doi.org/10.1108/ijse-05-2017-0194.

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Purpose The purpose of this paper is to examine the impact of financial inclusion on poverty alleviation through cooperative banks. Design/methodology/approach In order to fulfil the objectives of the study, primary data were collected from 540 beneficiaries of cooperative banks operating in three northern states of India, i.e., J&K, Himachal Pradesh (HP) and Punjab using purposive sampling during July-December 2015. The technique of factor analysis had been used for summarisation of the total data into minimum factors. For checking the validity and reliability of the data, the second-order CFA was performed. Statistical techniques like one-way ANOVA, t-test and SEM were used for data analysis. Findings The study results reveal that financial inclusion through cooperative banks has a direct and significant impact on poverty alleviation. The study highlights that access to basic financial services such as savings, loans, insurance, credit, etc., through financial inclusion has generated a positive impact on the lives of the poor and help them to come out of the clutches of poverty. Research limitations/implications The study was conducted amidst few limitations. First, the in-depth analysis of the study is restricted to three northern states only because of limited resources and time availability. Second, the study is limited to the perception of financial inclusion beneficiaries only, which, in future, could be carried further on the perception of other stakeholders such as bank officials, business correspondents, village panchayats, etc. Originality/value The study makes contribution towards the financial inclusion literature relating to poverty alleviation and fulfils the research gap to some extent by assessing the impact of financial inclusion on poverty alleviation through cooperative banks. This paper can help the policymakers and other stakeholders of cooperative banks in promoting banking habits among poor rural households both at the national and international level.

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Lin, Nankai, Jinxian Li, and Shengyi Jiang. "A simple but effective method for Indonesian automatic text summarisation." Connection Science, June 10, 2021, 1–15. http://dx.doi.org/10.1080/09540091.2021.1937942.

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