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Story Summarization Using a Question-Answering Approach

Story Summarization Using a Question-Answering Approach

Sanah Nashir Sayyed, Namrata Mahender C.
Copyright: © 2021 |Pages: 24
ISBN13: 9781799847304|ISBN10: 1799847306|EISBN13: 9781799847311
DOI: 10.4018/978-1-7998-4730-4.ch003
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MLA

Sayyed, Sanah Nashir, and Namrata Mahender C. "Story Summarization Using a Question-Answering Approach." Handbook of Research on Natural Language Processing and Smart Service Systems, edited by Rodolfo Abraham Pazos-Rangel, et al., IGI Global, 2021, pp. 46-69. https://doi.org/10.4018/978-1-7998-4730-4.ch003

APA

Sayyed, S. N. & C., N. M. (2021). Story Summarization Using a Question-Answering Approach. In R. Pazos-Rangel, R. Florencia-Juarez, M. Paredes-Valverde, & G. Rivera (Eds.), Handbook of Research on Natural Language Processing and Smart Service Systems (pp. 46-69). IGI Global. https://doi.org/10.4018/978-1-7998-4730-4.ch003

Chicago

Sayyed, Sanah Nashir, and Namrata Mahender C. "Story Summarization Using a Question-Answering Approach." In Handbook of Research on Natural Language Processing and Smart Service Systems, edited by Rodolfo Abraham Pazos-Rangel, et al., 46-69. Hershey, PA: IGI Global, 2021. https://doi.org/10.4018/978-1-7998-4730-4.ch003

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Abstract

Summarization is the process of selecting representative data to produce a reduced version of the given data with a minimal loss of information; so, it generally works on text, images, videos, and speech data. The chapter deals with not only concepts of text summarization (types, stages, issues, and criteria) but also with applications. The two main categories of approaches generally used in text summaries (i.e., abstractive and extractive) are discussed. Abstractive techniques use linguistic methods to interpret the text; they produce understandable and semantically equivalent sentences with a shorter length. Extractive techniques mostly rely on statistical methods for extracting essential sentences from the given text. In addition, the authors explore the SACAS model to exemplify the process of summarization. The SACAS system analyzed 50 stories, and its evaluation is presented in terms of a new measurement based on question-answering MOS, which is also introduced in this chapter.

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