CiteQA@CLSciSumm 2020
In academic publications, citations are used to build context for a concept by highlighting relevant aspects from reference papers. Automatically identifying referenced snippets can help researchers swiftly isolate principal contributions of scientific works. In this paper, we exploit the underlying structure of scientific articles to predict reference paper spans and facets corresponding to a citation. We propose two methods to detect citation spans - keyphrase overlap, BERT along with structural priors. We fine-tune FastText embeddings and leverage textual, positional features to predict citation facets.
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1A-Team / Martin-Luther-Universität Halle-Wittenberg@CLSciSumm 20
This document demonstrates our groups approach to the CL-SciSumm shared task 2020. There are three tasks in CL-SciSumm 2020. In Task 1a, we apply a Siamese neural network to identify the spans of text in the reference pa…
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We present the systems we submitted for the shared tasks of the Workshop on Scholarly Document Processing at EMNLP 2020. Our approaches to the tasks are focused on exploiting large Transformer models pre-trained on huge …
IR&TM-NJUST@CLSciSumm 20
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SentenceSentence SimilarityCitRet: A Hybrid Model for Cited Text Span Retrieval
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RetrievalSemantic Textual Similarity