基于篇章修辞结构的自动文摘连贯性研究

尽管抽取式自动文摘方法是目前自动文摘领域的主流方法,并且取得了长足的进步,但抽取式自动文摘形成的摘要由于缺乏句子之间的合理指代或篇章结构,使得文摘缺乏连贯性而影响可读性。为提高自动摘要的可读性,该文尝试将篇章修辞结构信息应用于中文自动文摘。首先,基于汉语篇章修辞结构抽取摘要,然后使用基于LSTM的方法对文本连贯性进行建模,并使用该模型对文摘的连贯性做出评价。实验结果表明: 在摘要抽取方面,基于篇章修辞结构的自动文摘相比于传统的抽取方法具有更好的ROUGE评价值;在使用基于LSTM连贯性模型评价摘要连贯性方面,篇章结构信息在自动抽取文摘时可以很好地提炼出文章的主旨,同时使摘要具有更好的结果。

Research on Automatic Summarization Coherence Based on Discourse Rhetoric Structure

In order to improve the readability of automatic summaries, this article attempts to apply the discourse rhetorical structure information to Chinese automatic summarization. First, abstracts are extracted based on the rhetorical structure of Chinese texts. Then the LSTM-based methods are adopted to evaluate the coherence of the abstracts. The experimental results show that, automatic abstraction based on discourse rhetorical structure has better ROUGE value than traditional methods. The coherence evaluation results show that the discourse structure information can help the system extract the subject of the article automatically.