跨领域文本摘要的评价

Liam Scanlon, Shiwei Zhang, Xiuzhen Zhang, M. Sanderson
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引用次数: 2

摘要

抽取-抽象混合摘要可以为长文档生成可读的、简洁的摘要。先提取后抽象和先提取后抽象是混合摘要的两种代表性方法。但它们的总体性能还有待于大规模实验的评估。我们从三个新颖的角度研究了两种最先进的混合摘要算法:我们将它们应用于以前从未尝试过的标题生成形式,我们通过在新闻域内和跨新闻域测试来评估算法的泛化性;我们将算法的自动评估与人类的比较判断进行了比较。研究发现,提取-抽象-混合方法优于提取-抽象方法,特别是对于跨域标题生成。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Evaluation of Cross Domain Text Summarization
Extractive-abstractive hybrid summarization can generate readable, concise summaries for long documents. Extraction-then-abstraction and extraction-with-abstraction are two representative approaches to hybrid summarization. But their general performance is yet to be evaluated by large scale experiments.We examined two state-of-the-art hybrid summarization algorithms from three novel perspectives: we applied them to a form of headline generation not previously tried, we evaluated the generalization of the algorithms by testing them both within and across news domains; and we compared the automatic assessment of the algorithms to human comparative judgments. It is found that an extraction-then-abstraction hybrid approach outperforms an extraction-with-abstraction approach, particularly for cross-domain headline generation.
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