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AbstractIn this paper, we propose a multi-granularity interaction network for extractive and abstractive multi-document summarization, which jointly learn semantic representations for words, sentences, and documents. The word representations are used …...

AbstractSelecting input features of top relevance has become a popular method for building self-explaining models. In this work, we extend this selective rationalization approach to text matching, where the goal …...

AbstractSentence fusion is a conditional generation task that merges several related sentences into a coherent one, which can be deemed as a summary sentence. The importance of sentence fusion has …...

ive Multi-Document Summarization Shuaiqi LIU, Jiannong Cao, Ruosong Yang, and Zhiyuan Wen Department of Computing, The Hong Kong Polytechnic University {cssqliu, csjcao, csryang, cszwen}@comp.polyu.edu.hk Abstract Abstractive multi-document summarization aims to …...

AbstractMost of existing extractive multi-document summarization (MDS) methods score each sentence individually and extract salient sentences one by one to compose a summary, which have two main drawbacks: (1) neglecting …...

AbstractMulti-document summarization (MDS) has made significant progress in recent years, in part facilitated by the availability of new, dedicated datasets and capacious language models. However, a standing limitation of these …...

AbstractA notable challenge in Multi-Document Summarization (MDS) is the extremely-long length of the input. In this paper, we present an extract-then-abstract Transformer framework to overcome the problem. Specifically, we leverage …...