Abstract
Automatic text summarisation, especially sentence extraction, has received a great deal of attention from researchers. However, a majority of the work focuses on newswire summarisation where the goal is to generate headlines or short summaries from a single news article or a cluster of related news articles. One primary reason for this is the fact that most public datasets related to text summarisation consist of newswire articles. Whether it is the traditional Document Understanding Conference (DUC) or Text Analysis Conference (TAC) datasets or the recent CNN/Daily mail corpus, the focus is mainly on newswire articles. In reality, this forms a rather small part of the numerous possible applications of text summarisation. The focus is now shifting towards other areas like product-review summarisation, domain-specific summarisation and real-time summarisation. Each of these areas have their own sets of challenges, but they have one issue in common, i.e. availability of large-scale corpora which can be used for supervised or semi-supervised learning. In this work, we highlight two such use cases, related to summarising legal and scientific articles, which are very different from the generic document summarisation tasks. We discuss how these are different from generic newswire summarisation, introduce two new corpora for these domains and propose new keyword based as well as neural sentence extraction techniques.
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ROUGE-1.5.5 with the parameters: -n 4 -m -a -l Z -x -c 95 -r 1000 -f A -p 0.5 -t 0.
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Mehta, P., Majumder, P. (2019). Domain-Specific Summarisation. In: From Extractive to Abstractive Summarization: A Journey. Springer, Singapore. https://doi.org/10.1007/978-981-13-8934-4_4
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DOI: https://doi.org/10.1007/978-981-13-8934-4_4
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