新闻文章的跨时间比较摘要

Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining Pub Date : 2019-01-30 DOI:10.1145/3289600.3291008

Yijun Duan, A. Jatowt

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引用次数: 23

摘要

比较摘要是一种有效的策略，可以发现偏向于用户兴趣的文档集合中的重要异同。这项任务的自然方法是找到重要的和相应的内容。本文提出了一种新的基于查询的新闻档案跨时间自动摘要研究任务，并给出了一种有效的方法来解决这一问题。该模型首先学习时间间隔较远的新闻集合之间的正交变换。然后，基于简洁的整数线性规划框架生成一组相应的句子对。通过实验验证了该方法在《纽约时报》标注语料库上的有效性。

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Across-Time Comparative Summarization of News Articles

Comparative summarization is an effective strategy to discover important similarities and differences in collections of documents biased to users' interests. A natural method of this task is to find important and corresponding content. In this paper, we propose a novel research task of automatic query-based across-time summarization in news archives as well as we introduce an effective method to solve this task. The proposed model first learns an orthogonal transformation between temporally distant news collections. Then, it generates a set of corresponding sentence pairs based on a concise integer linear programming framework. We experimentally demonstrate the effectiveness of our method on the New York Times Annotated Corpus.

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Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining

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