论文标题
以遥远的监督为以查询为重点的多文件摘要
Query Focused Multi-Document Summarization with Distant Supervision
论文作者
论文摘要
我们考虑了更好地建模查询群集交互的问题，以促进以查询为中心的多文件摘要（QFS）。由于缺乏培训数据，现有工作在很大程度上取决于检索式方法来估计查询和文本段之间的相关性。在这项工作中，我们利用遥远的监督回答回答各种资源的地方，以更明确地捕获查询和文档之间的关系。我们提出了一个粗到精细的建模框架，该框架引入了单独的模块，以估算段是否与查询相关，可能包含答案和中心。在此框架下，训练有素的证据估算器进一步辨别了检索到的细分市场的估计值可能会在摘要中回答最终选择的查询。我们证明，我们的框架在标准QFS基准测试上优于强大的比较系统。
We consider the problem of better modeling query-cluster interactions to facilitate query focused multi-document summarization (QFS). Due to the lack of training data, existing work relies heavily on retrieval-style methods for estimating the relevance between queries and text segments. In this work, we leverage distant supervision from question answering where various resources are available to more explicitly capture the relationship between queries and documents. We propose a coarse-to-fine modeling framework which introduces separate modules for estimating whether segments are relevant to the query, likely to contain an answer, and central. Under this framework, a trained evidence estimator further discerns which retrieved segments might answer the query for final selection in the summary. We demonstrate that our framework outperforms strong comparison systems on standard QFS benchmarks.
