Overview
Provides an overview of the transition from extractive to abstractive summarization and how they can be combined to create a practical summarization system
Covers in detail ways to build ensembles from several existing techniques
Places special emphasis on domain-specific applications that are practically useful
Is accompanied by source codes and corpora, so that readers can use the book as a springboard for future experiments
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Table of contents (8 chapters)
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Front Matter
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Back Matter
Keywords
About this book
In turn, the book describes the application of summarization in the legal and scientific domains, describing two new corpora that consist of more than 100 thousand court judgments and more than 20 thousand scientific articles, with the corresponding manually written summaries. The availability of these large-scale corpora opens up the possibility of using the now popular data-driven approaches based on deep learning. The book then highlights the effectiveness of neural sentence extraction approaches, which perform just as well as rule-based approaches, but without the need for any manual annotation. As a next step, multiple techniques for creating ensembles of sentence extractors – which deliver better and more robust summaries – are proposed. In closing, the book presents a neural network-based model for sentence compression. Overall the book takes readers on a journey that begins with simple sentence extraction and ends in abstractive summarization, while also covering key topics like ensemble techniques and domain-specific summarization, which have not been explored in detail prior to this.
Authors and Affiliations
About the authors
Dr. Mehta has served as reviewer for the journals Information Processing and Management and Forum for Information Retrieval Evaluation. Apart from several journal and conference papers, he has also co-edited a book on text processing published by Springer.
Prof. Prasenjit Majumder is an Associate Professor at Dhirubhai Ambani Institute of ICT (DA-IICT), Gandhinagar and a Visiting Professor at the Indian Institute of Information Technology, Vadodara (IIIT-V). Prof. Majumder completed his Ph.D. at Jadavpur University in 2008 and worked as a postdoctoral fellow at the University College Dublin, prior to joining the DA-IICT, where he currently heads the Information Retrieval and Language Processing Lab. His research interests lie at the intersection of Information Retrieval, Cognitive Science and Human Computing Interaction. He has headed several projects sponsored by the Govt. of India.
He is one of the pioneers of the Forum for Information Retrieval Evaluation (FIRE), which assesses research on Information Retrieval and related areas for South Asian languages. Since being founded in 2008, FIRE has grown to become a respected conference, drawing participants from across the globe. Prof. Majumder has authored several journal and conference papers, and co-edited two special issues of Transactions in Information Systems (ACM). He has co-edited two books: ‘Multi Lingual Information Access in South Asian Languages’ and ‘Text Processing,’ both published by Springer.
Bibliographic Information
Book Title: From Extractive to Abstractive Summarization: A Journey
Authors: Parth Mehta, Prasenjit Majumder
DOI: https://doi.org/10.1007/978-981-13-8934-4
Publisher: Springer Singapore
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer Nature Singapore Pte Ltd. 2019
Hardcover ISBN: 978-981-13-8933-7Published: 30 August 2019
Softcover ISBN: 978-981-13-8936-8Published: 30 August 2020
eBook ISBN: 978-981-13-8934-4Published: 13 August 2019
Edition Number: 1
Number of Pages: XI, 116
Number of Illustrations: 461 b/w illustrations, 9 illustrations in colour
Topics: Performance and Reliability, Computer Communication Networks, Information Systems Applications (incl. Internet)