A Sentence Compression Based Framework to Query-Focused Multi-Document Summarization
Lu Wang1 Hema Raghavan2 Vittorio Castelli2 Radu Florian2 Claire Cardie1 1Department of Computer Science, Cornell University, Ithaca, NY 14853, USA {luwang, 2IBM T. J. Watson Research Center, Yorktown Heights, NY 10598, USA {hraghav, vittorio,
Abstract
We consider the problem of using sentence compression techniques to facilitate query-focused multi-document summarization. We present a sentence-compression-based framework for the task, and design a series of learning-based compression models built on parse trees. An innovative beam search decoder is proposed to efficiently find highly probable compressions. Under this framework, we show how to integrate various indicative metrics such as linguistic motivation and query relevance into the compression process by deriving a novel formulation of a compression scoring function. Our best model achieves statistically significant improvement over the state-of-the-art systems on several metrics (e.g. 8.0% and 5.4% improvements in ROUGE-2 respectively) for the DUC 2006 and 2007 summarization task.
中文速览
面向用户查询的多文档自动摘要长期依赖"抽句子"的方式，但整句抽取往往把无关细节一并带入，既冗长又不够精准。这项研究提出了一套以句子压缩（sentence compression）为核心的摘要框架，设计了规则式、序列式和基于句法解析树的三类压缩模型，并创新性地引入束搜索（beam search）解码器，将查询相关性、语言质量、内容重要性和冗余度等多种指标融入压缩打分函数，让压缩过程直接服务于摘要目标。在DUC 2006和2007两个标准评测数据集上，最优模型的ROUGE-2分数分别提升了8.0%和5.4%，并在人工Pyramid评估和可读性评分上同样优于对比系统。这项工作首次在统计显著性层面证明了学习式句子压缩能够超越纯抽取方法，为生成更简洁、更贴近查询意图的摘要提供了切实可行的路径。
原文 arXiv:1606.07548;中英对照 + 大白话阅读 https://aha.fim.ai/paper/1606.07548v1