02/02/2021

Enhancing Scientific Papers Summarization with Citation Graph

Chenxin An, Ming Zhong, Yiran Chen, Danqing Wang, Xipeng Qiu, Xuanjing Huang

Keywords:

Abstract Paper Similar Papers

Abstract: Previous work for text summarization in scientific domain mainly focused on the content of the input document, but seldom considering its citation network. However, scientific papers are full of uncommon domain-specific terms, making it almost impossible for the model to understand its true meaning without the help of the relevant research community. In this paper, we redefine the task of scientific papers summarization by utilizing their citation graph and propose a citation graph-based summarization model CGSum which can incorporate the information of both the source paper and its references. In addition, we construct a novel scientific papers summarization dataset Semantic Scholar Network (SSN) which contains 141K research papers in different domains and 661K citation relationships. The entire dataset constitutes a large connected citation graph. Extensive experiments show that our model can achieve competitive performance when compared with the pretrained models even with a simple architecture. The results also indicates the citation graph is crucial to better understand the content of papers and generate high-quality summaries.

The video of this talk cannot be embedded. You can watch it here:
https://slideslive.com/38948135
(Link will open in new window)
 0
 0
 0
 0
  Share    
This is an embedded video. Talk and the respective paper are published at AAAI 2021 virtual conference. If you are one of the authors of the paper and want to manage your upload, see the question "My papertalk has been externally embedded..." in the FAQ section.

Comments

Post Comment
no comments yet
code of conduct: tbd Characters remaining: 140

Similar Papers

 0
 0
 0
 0
 17:58 
 0
 0
 0
 0
 4:51 
 0
 0
 0
 0
 15:36 
 0
 0
 0
 0
 12:21 
 0
 0
 0
 0
 31:24 
 0
 0
 0
 0
 6:38 
 0
 0
 0
 0
 11:03 
 0
 0
 0
 0
 11:23 
 0
 0
 0
 0
 7:54 
 0
 0
 0
 0
 15:10 
 0
 0
 0
 0
 13:54 
 0
 0
 0
 0
 4:57 
 0
 0
 0
 1
 10:15 
 0
 0
 0
 0
 2:48 
 0
 0
 0
 0
 6:48 
 0
 0
 0
 0
 10:01 
 0
 0
 0
 0
 4:45 
 0
 0
 0
 0
 5:15 
 0
 0
 0
 0
 6:21 
 0
 0
 0
 0
 15:50 
 0
 0
 0
 0
 6:36 
 0
 0
 0
 0
 4:58 
 0
 0
 0
 0
 11:52 
 0
 0
 0
 0
 16:27 
 0
 0
 0
 0
 3:01 
 0
 0
 0
 0
 11:41 
 0
 0
 0
 0
 6:14 
 0
 0
 0
 0
 2:56 
 0
 0
 0
 0
 19:10 
 0
 0
 0
 0
 5:43 
 0
 0
 0
 0
 12:18 
 0
 0
 0
 0
 11:04 
 0
 0
 0
 0
 10:19 
 0
 0
 0
 0
 19:41 
 0
 0
 0
 0
 14:22 
 0
 0
 0
 0
 10:48 
 0
 0
 0
 0
 19:39 
 0
 0
 0
 0
 3:24 
 0
 0
 0
 0
 10:01 
 0
 0
 0
 0
 11:28