The CL-SciSumm Shared Task is the first medium-scale shared task on scientific document summarization in the computational linguistics~(CL) domain. In 2019, it comprised three tasks: (1A) identifying relationships between citing documents and the referred document, (1B) classifying the discourse facets, and (2) generating the abstractive summary. The dataset comprised 40 annotated sets of citing and reference papers of the CL-SciSumm 2018 corpus and 1000 more from the SciSummNet dataset. All papers are from the open access research papers in the CL domain. This overview describes the participation and the official results of the CL-SciSumm 2019 Shared Task, organized as a part of the 42nd Annual Conference of the Special Interest Group in Information Retrieval (SIGIR), held in Paris, France in July 2019. We compare the participating systems in terms of two evaluation metrics and discuss the use of ROUGE as an evaluation metric. The annotated dataset used for this shared task and the scripts used for evaluation can be accessed and used by the community at: https://github.com/WING-NUS/scisumm-corpus.
翻译:CL-SciSumm 共享任务(CL-SciSumm Common Company)是计算语言-(CL)领域科学文件总结的第一个中等共享任务,在2019年,它由三项任务组成:(a) 确定引用的文件与参考文件之间的关系,(1B) 对讨论的方面进行分类,以及(2) 产生抽象摘要,数据集包括40套CL-SciSumm 2018系统附加说明的引文和参考文件,以及另外1000个SciSummNet数据集。所有文件都来自CL域的公开存取研究文件。本概览描述了作为2019年7月在法国巴黎举行的信息检索特别利益小组第四十二届年会的一部分而组织的CL-SciSumm 2019共同任务的参与情况和正式结果。我们用两个评价指标对参与系统进行比较,并讨论使用ROUGE作为评价指标。用于这一共同任务的附加说明数据集和用于评价的脚本可供社区查阅和使用。https://githubus-simsum.com/INGS。