Multiple Aspect Summarization Using Integer Linear Programming
Multiple Aspect Summarization Using Integer Linear Programming
复制标题
DOI：
--
复制
发表时间：
2012-07
期刊：
影响因子：
--
通讯作者：
K. Woodsend;Mirella Lapata
中科院分区：
文献类型：
--
作者：
K. Woodsend;Mirella Lapata
多文档自动文摘涉及到内容选择和表层实现等多个方面。摘要必须信息丰富、简洁、符合语法，并遵守文体写作惯例。我们提出了一种方法，其中这些单独的方面分别从数据中学习（没有任何手工工程），但使用整数线性规划联合优化。ILP框架允许我们联合收割机结合专家学习者的决策，并通过目标设置、软约束和硬约束的混合来选择和重写源内容。在TAC-08数据集上的实验结果表明，我们的模型使用ROUGE实现了最先进的性能，并显着提高了摘要的信息量。
Multi-document summarization involves many aspects of content selection and surface realization. The summaries must be informative, succinct, grammatical, and obey stylistic writing conventions. We present a method where such individual aspects are learned separately from data (without any hand-engineering) but optimized jointly using an integer linear programme. The ILP framework allows us to combine the decisions of the expert learners and to select and rewrite source content through a mixture of objective setting, soft and hard constraints. Experimental results on the TAC-08 data set show that our model achieves state-of-the-art performance using ROUGE and significantly improves the informativeness of the summaries.