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Extractive multi-document summarization based on textual entailment and sentence compression via knapsack problem

Published online by Cambridge University Press:  31 October 2018

ALI NASERASADI ,
HAMID KHOSRAVI  and
FARAMARZ SADEGHI
ALI NASERASADI
Affiliation:
Department of Applied Mathematics, Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman, Kerman, Iran e-mail: naserasadi@uk.ac.ir
HAMID KHOSRAVI
Affiliation:
Department of Computer Science, Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman, Kerman, Iran e-mails: hkhosravi@uk.ac.ir, farsad@uk.ac.ir
FARAMARZ SADEGHI
Affiliation:
Department of Computer Science, Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman, Kerman, Iran e-mails: hkhosravi@uk.ac.ir, farsad@uk.ac.ir
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Abstract

By increasing the amount of data in computer networks, searching and finding suitable information will be harder for users. One of the most widespread forms of information on such networks are textual documents. So exploring these documents to get information about their content is difficult and sometimes impossible. Multi-document text summarization systems are an aid to producing a summary with a fixed and predefined length, while covering the maximum content of the input documents. This paper presents a novel method for multi-document extractive summarization based on textual entailment relations and sentence compression via formulating the problem as a knapsack problem. In this approach, sentences of documents are ranked according to the extended Tf-Idf method, then entailment scores of selected sentences are computed. Through these scores, the final score of each sentence is calculated. Finally, by decreasing the lengths of sentences via sentence compression, the problem has been solved by greedy and dynamic Programming approaches to the knapsack problem. Experiments on standard summarization datasets and evaluating the results based on the Rouge system show that the suggested method, according to the best of our knowledge, has increased F-measure of query-based summarization systems by two per cent and F-measure of general summarization systems by five per cent.

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Article
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Natural Language Engineering , Volume 25 , Issue 1 , January 2019 , pp. 121 - 146
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Copyright © Cambridge University Press 2018 

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