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Conventional solutions to automatic related work summarization rely heavilyon human-engineered features. In this paper, we develop a neural data-drivensummarizer by leveraging the seq2seq paradigm, in which a joint context-drivenattention mechanism is proposed to measure the contextual relevance within fulltexts and a heterogeneous bibliography graph simultaneously. Our motivation isto maintain the topic coherency between a related work section and its targetdocument, where both the textual and graphic contexts play a big role incharacterizing the relationship among scientific publications accurately.Experimental results on a large dataset show that our approach achieves aconsiderable improvement over a typical seq2seq summarizer and five classicalsummarization baselines.

Neural Related Work Summarization with a Joint Context-driven Attention Mechanism