Abstract. This report describes our contribution to the 2nd Computational Linguistics Scientific Document Summarization Shared Task (CLSciSumm 2016), which asked to identify the relevant text span in a reference paper that corresponds to a citation in another document that cites this paper. We developed three different approaches based on summarisation and classification techniques. First, we applied a modified version of an unsupervised summarisation technique, TextSentenceRank, to the reference document, which incorporates the similarity of sentences to the citation on a textual level. Second, we employed classification to select from candidates previously extracted through the original TextSentenceRank algorithm. Third, we used unsupervised summarisation of the relevant sub-part of the document that was previously selected in a supervised manner.
REF applied a modified version of an unsupervised summarization technique (TextSentenceRank) to the reference document.
Identifying Referenced Text in Scientific Publications by Summarisation and Classification Techniques
BIRNDL@JCDL
Computer Science