Automatic related work section generation: experiments in scientific document abstracting

Ahmed AbuRa’ed (), Horacio Saggion (), Alexander Shvets () and Àlex Bravo ()
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Ahmed AbuRa’ed: Universitat Pompeu Fabra
Horacio Saggion: Universitat Pompeu Fabra
Alexander Shvets: Universitat Pompeu Fabra
Àlex Bravo: Universitat Pompeu Fabra

Scientometrics, 2020, vol. 125, issue 3, No 56, 3159-3185

Abstract: Abstract Related work sections or literature reviews are an essential part of every scientific article being crucial for paper reviewing and assessment. However, writing a good related work section is an activity which requires considerable expertise to identify, condense/summarize, and combine relevant information from different sources. In this work we compare different automatic methods to produce “descriptive” related work sections given as input the set of papers which need to be described. The main contribution of our work is a neural sequence learning process which produces citation sentences to be included in a related work section of an article. We train the neural architecture using an available scientific data set of citation sentences and we test over a data set of related work sections; we also compare the performance to a set of baseline extractive summarizers, an abstractive summarizer and a state of the art CNNs approach. Our results indicate that our approach outperforms the simple as well as the informed baselines.

Keywords: Scientific summarization; Document abstracting; Sequence learning; Information extraction from scientific literature; 68T50; 97R40; I.2.7 (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s11192-020-03630-2

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