论文标题
在任何方面汇总文本：一种知识知情的弱监督方法
Summarizing Text on Any Aspects: A Knowledge-Informed Weakly-Supervised Approach
论文作者
论文摘要
给定一个文档和目标方面（例如，感兴趣的主题），基于方面的抽象摘要试图对该方面产生摘要。先前的研究通常假设一小部分预定的方面，并且没有汇总其他不同主题。在这项工作中，我们研究了与文档相关的任意方面的总结，该方面大大扩展了任务在实践中的应用。由于缺乏监督数据，我们开发了一种新的弱监督构建方法和一个方面建模方案，这两者都整合了富裕的外部知识来源，例如概念网和Wikipedia。实验表明，我们的方法可以提高绩效，从而总结了预定义或任意方面的真实和合成文件。
Given a document and a target aspect (e.g., a topic of interest), aspect-based abstractive summarization attempts to generate a summary with respect to the aspect. Previous studies usually assume a small pre-defined set of aspects and fall short of summarizing on other diverse topics. In this work, we study summarizing on arbitrary aspects relevant to the document, which significantly expands the application of the task in practice. Due to the lack of supervision data, we develop a new weak supervision construction method and an aspect modeling scheme, both of which integrate rich external knowledge sources such as ConceptNet and Wikipedia. Experiments show our approach achieves performance boosts on summarizing both real and synthetic documents given pre-defined or arbitrary aspects.
