基于序列到序列模型的抽象式中文文本摘要研究

目的/意义]为更好地处理文本摘要任务中的未登录词(out of vocabulary, 00V ),同时避免摘要重复,提高文本摘要的质量,本文以解决00V问题和摘要自我重复问题为研究任务,进行抽象式中文文本摘要研究。方法/过程]在序列到序列(sequence to sequence, seq2seq)模型的基础上增加指向生成机制和覆盖处理机制,通过指向生成将未登录词拷贝到摘要中以解决未登录词问题,通过覆盖处理避免注意力机制(attentionmechanism)反复关注同一位置,以解决重复问题。将本文方法应用到LCSTS中文摘要数据集上进行实验,检验模型效果。结果/结论]实验结果显示,该模型生成摘要的ROUGE ( recall -oriented understudy for gisting evaluation)分数高于传统的seq2seq模型以及抽取式文本摘要模型,表明指向生成和覆盖机制能够有效解决未登录词问题和摘要重复问题,从而显著提升文本摘要质量。

Research of Abstractive Chinese Text Summarization Based on Seq2seq Model

Purpose/significance] To deal with the Out Of Vocabulary (OOV) in text summarization while avoiding duplication of summaries, this article focuses on solving the OOV problem and the self-duplication and carries out a profiling study.Method/process] Bases on the sequence-to-sequence model, a pointer generator module and a coverage processing module are added. An attempt is made to copy the OOV into abstractive summary to solve the problem of OOV by means of the pointer generator module. The coverage processing module tries to avoid the Attention Mechanism paying attention to the same position repeatedly to solve the duplicate problem. The model is applied to the Chinese summarization dataset LCSTS to conduct experiments to test the effectiveness.Result/conclusion] Experiment results show that the ROUGE of the generated summary is much higher than that of seq2seq model and extractive model, indicating that in the abstractive Chinese text summary, the pointer generator module and the coverage mechanism module can effectively solve the problem of OOV and the repetition of the summary, thereby significantly improving text summary quality.