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 . In scientific fields , scholars need to contextualize their contribution to help readers acquire an understanding of their research papers . For this purpose , the related work section of an article serves as a pivot to connect prior domain knowledge , in which the innovation and superiority of current work are displayed by a comparison with previous studies . While citation prediction can assist in drafting a reference collection ( Nallapati et al . , 2008 ) , consuming all these papers is still a laborious job , where authors must read every source document carefully and locate the most relevant content cautiously . As a solution in saving authors ' efforts , automatic related work summarization is essentially a topic-biased multi-document problem ( Cong and Kan , 2010 ) , which relies heavily on humanengineered features to retrieve snippets from the references . Most recently , neural networks enable a data-driven architecture sequence-to-sequence ( seq2seq ) for natural language generation ( Bahdanau et al . , 2014 ( Bahdanau et al . , , 2016 ) ) , where an encoder reads a sequence of words / sentences into a context vector , from which a decoder yields a sequence of specific outputs . Nonetheless , compared to scenarios like machine translation with an end-to-end nature , aligning a related work section to its source documents is far more challenging . To address the summarization alignment , former studies try to apply an attention mechanism to measure the saliency / novelty of each candidate word / sentence ( Tan et al . , 2017 ) , with the aim of locating the most representative content to retain primary coverage . However , toward summarizing a related work section , authors should be more creative when organizing text streams from the reference collection , where the selected content ought to highlight the topic bias of current work , rather than retell each reference in a compressed but balanced fashion . This motivates us to introduce the contextual relevance and characterize the relationship among scientific publications accurately . Generally speaking , for a pair of documents , a larger lexical overlap often implies a higher similarity in their research backgrounds . Yet such a hypothesis is not always true when sampling content from multiple relevant topics . Take " DSSM " 1 as an example , from viewpoint of the abstract similarity , those references investigating " Information Retrieval " , " Latent Semantic Model " or " Clickthrough Data Mining " could be of more importance in correlation and should be greatly sampled for the related work section . But in reality , this article spends a bit larger chunk of texts ( about 58 % ) to elaborate " Deep Learning " during the literature review , which is quite difficult for machines to grasp the contextual relevance therein . In addi-tion , other situations like emerging new concepts also suffer from the terminology variation or paraphrasing in varying degrees . In this study , we utilize a heterogeneous bibliography graph to embody the relationship within a scalable scholarly database . Over the recent past , there is a surge of interest in exploiting diverse relations to analyze bibliometrics , ranging from literature recommendation ( Yu et al . , 2015 ) to topic evolvement ( Jensen et al . , 2016 ) . In a graphical sense , interconnected papers transfer the credit among each other directly / indirectly through various patterns , such as paper citation , author collaboration , keyword association and releasing on series of venues , which constitutes the graphic context for outlining concerned topics . Unfortunately , a variety of edge types may pollute the information inquiry , where a slice of edges are not so important as the others on sampling content . Meanwhile , most existing solutions in mining heterogeneous graphs depend on the human supervision , e.g. , hyperedge ( Bu et al . , 2010 ) and metapath ( Swami et al . , 2017 ) . This is usually not easy to access due to the complexity of graph schemas . Our contribution is threefold : First , we explore the edge-type usefulness distribution ( EUD ) on a heterogeneous bibliography graph , which enables the relationship discovery ( between any pair of papers ) for sampling the interested information . Second , we develop a novel seq2seq summarizer for the automatic related work summarization , where a joint context-driven attention mechanism is proposed to measure the contextual relevance within both textual and graphic contexts . Third , we conduct experiments on 8,080 papers with native related work sections , and experimental results show that our approach outperforms a typical seq2seq summarizer and five classical summarization baselines significantly . In this paper , we highlight the contextual relevance for the automatic related work summarization , and analyze the graphic context to characterize the relationship among scientific publications accurately . We develop a neural data-driven summarizer by leveraging the seq2seq paradigm , where a joint context-driven attention mechanism is proposed to measure the contextual relevance within full texts and a heterogeneous bibliography graph simultaneously . Extensive experiments demonstrate the validity of the proposed attention mechanism , and the superiority of our approach over six representative summarization baselines . In future work , an appealing direction is to organize the selected sentences in a logical fashion , e.g. , by leveraging a topic hierarchy tree to determine the arrangement of the related work section ( Cong and Kan , 2010 ) . We also would like to take the citation sentences of each reference into consideration , which is another concise and universal data source for scientific summarization ( Chen and Hai , 2016 ; Cohan and Goharian , 2017 ) . At the end of this paper , we believe that extractive methods are by no means the final solutions for literature review generation due to plagiarism concerns , and we are going to put forward a fully abstractive version in further studies .

Challenge: Existing topic-based multi-document approaches on automatic related work summarization rely heavily on human-engineered features to retrieve snippets from the references.
Approach: They propose a neural data-driven seq2seq summarizer with a joint context-driven attention mechanism which measures contextual relevances within full texts and a heterogeneous bibliography graph.
Outcome: The proposed model outperforms a typical seq2seq and five classical baseline models on experiments with 8080 papers with native related work sections.