Using Argumentative Zones for Extractive Summarization of Scientific Articles

Information structure, i.e the way speakers construct sentences to present new information in the context of old, can capture rich linguistic information about the discourse structure of scientific documents. Information structure has been found useful for important Natural Language Processing (NLP) tasks, such as information retrieval and extraction. Since scientific articles typically follow a certain discourse structure describing the prior work, problem being solved, methods used, and so forth, it could also be useful for summarization of these articles. In this work we focus on a scheme of information structure called Argumentative Zoning (AZ), and investigate whether its categories could support extractive text summarization in a scientific domain. We develop a summarization system that uses AZ categories (i) as features and (ii) in the final sentence selection process. We evaluate the system directly as well as using task-based evaluation. The results show that AZ can support both full document and customized summarization. We report a statistically significant improvement in summarization performance against a competitive baseline that uses journal section labels instead of AZ information. TITLE AND ABSTRACT IN MANDARIN 一种根据“论证结构”自动摘录科技文献的方法 信息结构是指作者组织语句陈述信息的方式。信息结构例如科技文献的篇章结构包含丰富 的语言信息,有助于解决自然语言处理领域的一些重要问题例如信息检索和信息提取等。 科技文献通常使用特定的篇章结构来陈述以往的研究,阐述研究问题以及研究方法等等, 这些篇章结构可以被用于文献的自动摘录。本文着眼于一类特定的信息结构—“论证结构 ”,研究其是否有助于更好地摘录科技文献。在本文开发的摘录系统中,“论证结构”有 两种用途:一是作为特征供机器学习,二是用于最终的语句筛选过程。本文对该系统进行 了直接和间接的评测,测试结果显示“论证结构”有助于更好地对全文或指定信息进行摘 录。基于“论证结构”的摘录系统显著性优于基于章节标题的摘录系统。