QBSUM: A large-scale query-based document summarization dataset from real-world applications

Author:

Zhao Mingjun,Yan Shengli,Liu Bang,Zhong Xinwang,Hao Qian,Chen Haolan,Niu Di,Long Bowei,Guo Weidong

Publisher

Elsevier BV

Subject

Human-Computer Interaction,Theoretical Computer Science,Software

Reference39 articles.

1. Cao, Z., Li, W., Li, S., Wei, F., Li, Y., 2016. AttSum: joint learning of focusing and summarization with neural attention. arXiv:1604.00125.

2. The use of MMR, diversity-based reranking for reordering documents and producing summaries.;Carbonell,1998

3. Deep communicating agents for abstractive summarization;Celikyilmaz,2018

4. Coarse-to-fine question answering for long documents;Choi,2017

5. Abstractive sentence summarization with attentive recurrent neural networks;Chopra,2016

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