摘要
Automatically generating a brief summary for legal-related public opinion news(LPO-news,which contains legal words or phrases)plays an important role in rapid and effective public opinion disposal.For LPO-news,the critical case elements which are significant parts of the summary may be mentioned several times in the reader comments.Consequently,we investigate the task of comment-aware abstractive text summarization for LPO-news,which can generate salient summary by learning pivotal case elements from the reader comments.In this paper,we present a hierarchical comment-aware encoder(HCAE),which contains four components:1)a traditional sequenceto-sequence framework as our baseline;2)a selective denoising module to filter the noisy of comments and distinguish the case elements;3)a merge module by coupling the source article and comments to yield comment-aware context representation;4)a recoding module to capture the interaction among the source article words conditioned on the comments.Extensive experiments are conducted on a large dataset of legal public opinion news collected from micro-blog,and results show that the proposed model outperforms several existing state-of-the-art baseline models under the ROUGE metrics.
基金
supported by the National Key Research and Development Program of China (2018YFC0830105,2018YFC 0830101,2018YFC0830100)
the National Natural Science Foundation of China (Grant Nos.61972186,61762056,61472168)
the Yunnan Provincial Major Science and Technology Special Plan Projects (202002AD080001)
the General Projects of Basic Research in Yunnan Province (202001AT070046,202001AT070047).