Multi-Document Summarization Model Based on Redundancy-Constrained Knapsack Problem

Nishikawa, Hitoshi; Hirao, Tsutomu; Makino, Tsukasa; Matsuo, Yuichi

doi:10.5715/jnlp.20.585

Cited by 2 publications

(2 citation statements)

References 19 publications

(24 reference statements)

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“…More complex methods are proposed to find a trade-off between the coverage and the conciseness of the generated summary. For instance, Nishikawa et al [2012] model text summarization as a knapsack problem to generate a summary with a maximum coverage, selecting sentences with as many information units (unigrams and bigrams) as possible. Then, they add a constraint to prevent adding a unit that already exists in the summary.…”

Section: Summary Concisenessmentioning

confidence: 99%

Automatic text summarization: What has been done and what has to be done

Aries,

Zegour,

Hidouci

2019

Preprint

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Summaries are important when it comes to process huge amounts of information. Their most important benefit is saving time, which we do not have much nowadays. Therefore, a summary must be short, representative and readable. Generating summaries automatically can be beneficial for humans, since it can save time and help selecting relevant documents. Automatic summarization and, in particular, Automatic text summarization (ATS) is not a new research field; It was known since the 50s. Since then, researchers have been active to find the perfect summarization method. In this article, we will discuss different works in automatic summarization, especially the recent ones. We will present some problems and limits which prevent works to move forward. Most of these challenges are much more related to the nature of processed languages. These challenges are interesting for academics and developers, as a path to follow in this field.

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Section: Summary Concisenessmentioning

confidence: 99%

Automatic text summarization: What has been done and what has to be done

Aries,

Zegour,

Hidouci

2019

Preprint

View full text Add to dashboard Cite

show abstract

“…In Riedhammer et al (2008), Takamura and Okumura (2009) and Hirao et al (2013), this formulation has been used for extractive text summarization, but all of them have focused on single-document summarization. Also, in Nishikawa et al (2012), redundancy constrained knapsack problem has been used for document summarization.…”

Section: Background and Literature Reviewmentioning

confidence: 99%

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

2018

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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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Multi-Document Summarization Model Based on Redundancy-Constrained Knapsack Problem

Cited by 2 publications

References 19 publications

Automatic text summarization: What has been done and what has to be done

Automatic text summarization: What has been done and what has to be done

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

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