论文标题
Morty：从学术文章中提取有针对性信息的结构化汇总
MORTY: Structured Summarization for Targeted Information Extraction from Scholarly Articles
论文作者
论文摘要
从学术文章中提取信息是一项具有挑战性的任务，这是由于隐藏在文本，数字和引用中的大量文档长度和隐式信息。学术信息提取在数字库和知识管理系统的探索，档案和策展服务中都有各种应用。我们提出Morty，这是一种信息提取技术，可从学术文章中创建文本的结构化摘要。我们的方法将文章的全文凝结到属性值对，作为一个分割的文本段，称为结构化摘要。我们还提出了一个相当大的学术数据集，该数据集结合了从学术知识图和相应的公开科学文章中检索到的结构化摘要，我们公开发布了研究社区的资源。我们的结果表明，结构化摘要是针对目标信息提取的合适方法，可以补充其他常用方法，例如问答和命名实体识别。
Information extraction from scholarly articles is a challenging task due to the sizable document length and implicit information hidden in text, figures, and citations. Scholarly information extraction has various applications in exploration, archival, and curation services for digital libraries and knowledge management systems. We present MORTY, an information extraction technique that creates structured summaries of text from scholarly articles. Our approach condenses the article's full-text to property-value pairs as a segmented text snippet called structured summary. We also present a sizable scholarly dataset combining structured summaries retrieved from a scholarly knowledge graph and corresponding publicly available scientific articles, which we openly publish as a resource for the research community. Our results show that structured summarization is a suitable approach for targeted information extraction that complements other commonly used methods such as question answering and named entity recognition.
