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      A Supervised Approach to Extractive Summarisation of Scientific Papers

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          Abstract

          Automatic summarisation is a popular approach to reduce a document to its main arguments. Recent research in the area has focused on neural approaches to summarisation, which can be very data-hungry. However, few large datasets exist and none for the traditionally popular domain of scientific publications, which opens up challenging research avenues centered on encoding large, complex documents. In this paper, we introduce a new dataset for summarisation of computer science publications by exploiting a large resource of author provided summaries and show straightforward ways of extending it further. We develop models on the dataset making use of both neural sentence encoding and traditionally used summarisation features and show that models which encode sentences as well as their local and global context perform best, significantly outperforming well-established baseline methods.

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          Most cited references13

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          A Neural Attention Model for Abstractive Sentence Summarization

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            The Automatic Creation of Literature Abstracts

            H. P. Luhn (1958)
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              Summarizing Scientific Articles: Experiments with Relevance and Rhetorical Status

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                Author and article information

                Journal
                2017-06-13
                Article
                1706.03946
                1b0ba1f1-575e-44f9-9305-a5b1fe479efd

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

                History
                Custom metadata
                11 pages, 6 figures
                cs.CL cs.AI cs.NE stat.AP stat.ML

                Theoretical computer science,Applications,Machine learning,Neural & Evolutionary computing,Artificial intelligence

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