Focused Meeting Summarization via Unsupervised Relation Extraction

SIGDIAL '12: Proceedings of the 13th Annual Meeting of the Special Interest Group on Discourse and Dialogue(2016)

引用 4|浏览27
暂无评分
摘要
We present a novel unsupervised framework for focused meeting summarization that views the problem as an instance of relation extraction. We adapt an existing in-domain relation learner (Chen et al., 2011) by exploiting a set of task-specific constraints and features. We evaluate the approach on a decision summarization task and show that it outperforms unsupervised utterance-level extractive summarization baselines as well as an existing generic relation-extraction-based summarization method. Moreover, our approach produces summaries competitive with those generated by supervised methods in terms of the standard ROUGE score.
更多
查看译文
关键词
decision summarization task,existing generic relation-extraction-based summarization,focused meeting summarization,utterance-level extractive summarization baselines,existing in-domain relation learner,novel unsupervised framework,relation extraction,standard ROUGE score,supervised method,task-specific constraint,Focused meeting summarization,unsupervised relation extraction
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要