A Survey on Multi-document Summarization and Domain-Oriented Approaches

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Afsharizadeh Mahsa; EBRAHIMPOUR KOMLEH HOSSEIN; BAGHERI AYOUB; Chrupała Grzegorz

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Journal Paper

Paper Information

Journal: JOURNAL OF INFORMATION SYSTEMS AND TELECOMMUNICATION Year:2022 | Volume:10 | Issue:1 (37) Page(s): 68-79

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Title

A Survey on Multi-document Summarization and Domain-Oriented Approaches

Author(s)

Afsharizadeh Mahsa | EBRAHIMPOUR KOMLEH HOSSEIN | BAGHERI AYOUB | Chrupała Grzegorz | Issue Writer Certificate

Keywords

Multi-Document Summarization

Single Document Summarization

Extractive

Abstractive

Domain-Oriented

ROUGE

Abstract

Before the advent of the World Wide Web, lack of information was a problem. But with the advent of the web today, we are faced with an explosive amount of information in every area of search. This extra information is troublesome and prevents a quick and correct decision. This is the problem of information overload. Multi-document summarization is an important solution for this problem by producing a brief summary containing the most important information from a set of documents in a short time. This summary should preserve the main concepts of the documents. When the input documents are related to a specific domain, for example, medicine or law, summarization faces more challenges. Domain-oriented summarization methods use special characteristics related to that domain to generate summaries. This paper introduces the purpose of multi-document summarization systems and discusses domain-oriented approaches. Various methods have been proposed by researchers for multi-document summarization. This survey reviews the categorizations that authors have made on multi-document summarization methods. We also categorize the multi-document summarization methods into six categories: machine learning, clustering, graph, Latent Dirichlet Allocation (LDA), optimization, and deep learning. We review the different methods presented in each of these groups. We also compare the advantages and disadvantages of these groups. We have discussed the standard datasets used in this field, evaluation measures, challenges and recommendations.

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APA: Copy

Afsharizadeh, Mahsa, EBRAHIMPOUR KOMLEH, HOSSEIN, BAGHERI, AYOUB, & Chrupała, Grzegorz. (2022). A Survey on Multi-document Summarization and Domain-Oriented Approaches. JOURNAL OF INFORMATION SYSTEMS AND TELECOMMUNICATION (JIST), 10(1 (37)), 68-79. SID. https://sid.ir/paper/989896/en

Vancouver: Copy

Afsharizadeh Mahsa, EBRAHIMPOUR KOMLEH HOSSEIN, BAGHERI AYOUB, Chrupała Grzegorz. A Survey on Multi-document Summarization and Domain-Oriented Approaches. JOURNAL OF INFORMATION SYSTEMS AND TELECOMMUNICATION (JIST)[Internet]. 2022;10(1 (37)):68-79. Available from: https://sid.ir/paper/989896/en

IEEE: Copy

Mahsa Afsharizadeh, HOSSEIN EBRAHIMPOUR KOMLEH, AYOUB BAGHERI, and Grzegorz Chrupała, “A Survey on Multi-document Summarization and Domain-Oriented Approaches,” JOURNAL OF INFORMATION SYSTEMS AND TELECOMMUNICATION (JIST), vol. 10, no. 1 (37), pp. 68–79, 2022, [Online]. Available: https://sid.ir/paper/989896/en

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