Conventional solutions to automatic related work summarization rely heavily on humanengineered features. In this paper, we develop a neural data-driven summarizer by leveraging the seq2seq paradigm, in which a joint context-driven attention mechanism is proposed to measure the contextual relevance within full texts and a heterogeneous bibliography graph simultaneously. Our motivation is to maintain the topic coherency between a related work section and its target document, where both the textual and graphic contexts play a big role in characterizing the relationship among scientific publications accurately. Experimental results on a large dataset show that our approach achieves a considerable improvement over a typical seq2seq summarizer and five classical summarization baselines.
More recently, REF developed a neural data-driven summarizer with a joint context-driven attention mechanism to generate related work section.
Neural Related Work Summarization with a Joint Context-driven Attention Mechanism
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Computer Science