A Model for Text Summarization

Abstract

Text summarization is a process for creating a concise version of document(s) preserving its main content. In this paper, to cover all topics and reduce redundancy in summaries, a two-stage sentences selection method for text summarization is proposed. At the first stage, to discover all topics the sentences set is clustered by using k-means method. At the second stage, optimum selection of sentences is proposed. From each cluster the salient sentences are selected according to their contribution to the topic (cluster) and their proximity to other sentences in cluster to avoid redundancy in summaries until the appointed summary length is reached. Sentence selection is modeled as an optimization problem. In this study, to solve the optimization problem an adaptive differential evolution with novel mutation strategy is employed. With a test on benchmark DUC2001 and DUC2002 data sets, the ROUGE value of summaries got by the proposed approach demonstrated its validity, compared to the traditional methods of sentence selection and the top three performing systems for DUC2001 and DUC2002.

Short text similarity measurement methods: a review

“…The short text similarity is a topic that has been studied in the field of computer science, especially language processing. It plays an important role in many applications within natural language processing (NLP) and related areas such as question and answering systems (Aouicha et al 2018), a conversational agent in the business, gene clustering in biomedical, text summarization (Alguliyev et al 2017), web page, web image retrieval and plagiarism detection (Abdi et al 2017), essay scoring, information retrieval, text classification and text clustering (Abualigah et al 2017(Abualigah et al , 2018aAbualigah 2019). On the other hand, with the express growth of online social network, users have joined these networks.…”

Extractive multi document summarization using harmony search algorithm

“…However, the restriction of generic summarization is never a topic or query is available for the summarization procedure. While in query-based summarization, a summary is created depending on the query of the user, where the documents are searched to be matched with such query [8]. This paper approach supposes a new model for extractive generic MDS based on harmony search algorithm (HSA) that improves coverage, diversity, and readability.…”

Investigating Entropy for Extractive Document Summarization

“…Automatic document summarization is one such task with a compelling ability to combat the problem of information overload. Proposed more than six decades ago by Luhn (1958), modest progress in the area of automatic summarization is evident by moderate scores achieved by sophisticated deep neural methods (Fang et al, 2017;Nallapati et al, 2017;Dong et al, 2018;Zhou et al, 2018;Narayan et al, 2018;Yasunaga et al, 2019;Alguliyev et al, 2019;Dou et al, 2021;Zhong et al, 2020a;.…”

“…Existing extractive summarization methods rely on the intuitive notions of non-redundancy, relevance, and informativeness as signals of appositeness for inclusion of sentences in summary. While non-redundancy and relevance have been modeled in several earlier works (Luo et al, 2010;Alguliev et al, 2011;Gupta et al, 2014;Parveen et al, 2015;Nallapati et al, 2016Nallapati et al, , 2017Huang, 2017;Alguliyev et al, 2019;Saini et al, 2019), the notion of informativeness of a sentence is completely unattended. Despite sophisticated supervised and unsupervised techniques, existing approaches for document summarization are fundamentally devoid of any theory of importance.…”