关键词: 文档级文本摘要, 抽取式摘要, 端到端框架, 注意力机制, Pointer

Abstract:

Text summarization technology filters out important information from the text and presents it reasonably, which can help people quickly obtain information. In the field of Chinese single-document summarization, the supervised summarization model is not mature due to the lack of reliable data sets. A Chinese document-level summary corpus—CDESD（Chinese Document-level Extractive Summarization Dataset） with a scale of more than 200,000 articles is constructed, and a supervised document-level extractive summary model—DSum-SSE（Document Summarization with SPA Sentence Embedding） is proposed. The model is based on a neural network framework, and uses a sequence-to-sequence framework that combines Pointer and attention mechanisms to solve sentence-level generative summarization problems to obtain a representation vector that reflects the core meaning of the sentence, and introduce extremes on this basis Pointer mechanism, complete the supervised document-level extractive summary algorithm. Experiments show that compared with the popular unsupervised document-level extractive summary algorithm—TextRank, DSum-SSE is capable of providing higher-quality summaries. The corpus CDESD and the model DSum-SSE complement well in the field of Chinese document level summaries.

Key words: document-level summarization, extractive summary, sequence-to-sequence, attention mechanism, Pointer