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

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.

Unsupervised extractive multi-document summarization method based on transfer learning from BERT multi-task fine-tuning

“…Moreover, fine-tuning BERT on MNLI or QNLI has obtained comparable performance. These results might be due to the fact that the latter are among high-quality labelled corpus, designed for textual entailment tasks, which might constitute a class of problems relevant to text summarization task [72,73]. Moreover, Fine-tuning BERT on single sentence classification tasks (CoLA and SST-2) has shown promising results.…”

Extractive Multi-Document Summarization: A Review of Progress in the Last Decade

“…The three most essential points the best summary must contain are coverage, non-redundancy, and relevance. To achieve such a summary, the authors [73] [74] used textual entailment relations and sentence compression by the Knapsack problem. It is used to address the extractive MDS problem.…”

Automatic summarisation: 25 years On

“…Knight and Marcu (2002) adapt the noisy channel used in Statistical Machine Translation in order to develop a method for sentence compression which is seen as a first step towards producing summaries automatically. The summarisation process was also seen as an optimisation problem in (Naserasadi, Khosravi, and Sadeghi, 2019) where weights are learnt from the data.…”