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Inclusive Review on Extractive and Abstractive Text Summarization: Taxonomy, Datasets, Techniques and Challenges

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Intelligent Systems Design and Applications (ISDA 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 716))

Abstract

Condensing a lengthy text into a manageable length while maintaining the essential informational components and the meaning of the content is known as summarization. Manual text summarizing is a time-consuming and generally arduous activity that is becoming more and more popular, which is a major driving force behind academic research. Automatic Text summarization (ATS) has significant uses in a variety of Natural Language Processing (NLP) related activities, including text classification, question answering, summarizing legal texts, and news, and creating headlines. This is an emerging research field where most researchers are involved from popular companies namely, Google, Microsoft, Facebook, etc. This motivates us to present an inclusive review of extractive and abstractive summarization techniques for various inputs. In this paper, we are presenting a comparative study of different models, classified based on their techniques used. We have also classified them based on the dataset used at some places for better under-standing and the parametric evaluation of these techniques and their challenges have also been presented. Thus, the study presents a clear-cut view of the happenings of text summarization techniques and provides a roadmap for new re-searchers in this field.

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Author information

Authors and Affiliations

  1. GIET University, Gunupur, 765022, Odisha, India

    Gitanjali Mishra & Nilambar Sethi

  2. School of Information Technology and Engineering, VIT Vellore, Vellore, 632014, TN, India

    L. Agilandeeswari

Authors
  1. Gitanjali Mishra
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  2. Nilambar Sethi
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  3. L. Agilandeeswari
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Corresponding authors

Correspondence to Gitanjali Mishra or L. Agilandeeswari .

Editor information

Editors and Affiliations

  1. Faculty of Computing and Data Science, FLAME University, Pune, Maharashtra, India

    Ajith Abraham

  2. Center for Smart Computing Continuum, Burgenland, Austria

    Sabri Pllana

  3. University of Bari, Bari, Italy

    Gabriella Casalino

  4. University of Jinan, Jinan, Shandong, China

    Kun Ma

  5. Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab, India

    Anu Bajaj

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Mishra, G., Sethi, N., Agilandeeswari, L. (2023). Inclusive Review on Extractive and Abstractive Text Summarization: Taxonomy, Datasets, Techniques and Challenges. In: Abraham, A., Pllana, S., Casalino, G., Ma, K., Bajaj, A. (eds) Intelligent Systems Design and Applications. ISDA 2022. Lecture Notes in Networks and Systems, vol 716. Springer, Cham. https://doi.org/10.1007/978-3-031-35501-1_7

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