CiteSum: Citation Text-guided Scientific Extreme Summarization and Domain Adaptation with Limited Supervision
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Yuning Mao, Ming Zhong, Jiawei Han
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EMNLP 2022. TLDR: By pretraining on (automatically extracted) citation sentences in scientific papers, we achieve SOTA on SciTLDR, XSum, and Gigaword in zero-shot and (or) few-shot settings
Scientific extreme summarization (TLDR) aims to form ultra-short summaries of scientific papers. Previous efforts on curating scientific TLDR datasets failed to scale up due to the heavy human annotation and domain expertise required. In this paper, we propose a simple yet effective approach to automatically extracting TLDR summaries for scientific papers from their citation texts. Based on the proposed approach, we create a new benchmark CiteSum without human annotation, which is around 30 times larger than the previous human-curated dataset SciTLDR. We conduct a comprehensive analysis of CiteSum, examining its data characteristics and establishing strong baselines. We further demonstrate the usefulness of CiteSum by adapting models pre-trained on CiteSum (named CITES) to new tasks and domains with limited supervision. For scientific extreme summarization, CITES outperforms most fully-supervised methods on SciTLDR without any fine-tuning and obtains state-of-the-art results with only 128 examples. For news extreme summarization, CITES achieves significant gains on XSum over its base model (not pre-trained on CiteSum), e.g., +7.2 ROUGE-1 zero-shot performance and state-of-the-art few-shot performance. For news headline generation, CITES performs the best among unsupervised and zero-shot methods on Gigaword. Our dataset and code can be found at https://github.com/morningmoni/CiteSum.
A Hybrid Citation Retrieval Algorithm for Evidence-based Clinical Knowledge Summarization: Combining Concept Extraction, Vector Similarity and Query Expansion for High Precision
Northwestern University Feinberg School of Medicine（西北大学范伯格医学院）；Bluhm Cardiovascular Institute of Northwestern Medicine（西北大学布鲁姆心血管研究所）；University of Kansas Medical Center（堪萨斯大学医学中心）
A comprehensive review of automatic text summarization techniques: method, data, evaluation and coding
Universidade de Brasília（巴西利亚大学）；Nacional Institute of Science and Technology for Complex Systems（国家复杂系统科学技术研究所）；Instituto Federal de Minas Gerais（米纳斯吉拉斯联邦学院）
SKT5SciSumm -- Revisiting Extractive-Generative Approach for Multi-Document Scientific Summarization
University of Information Technology（信息科技大学）；VNU - HCM（胡志明市国家大学）；Deakin University（迪肯大学）；National Institute of Informatics（情报学研究所）；University of Göttingen（哥廷根大学）；University of Würzburg（维尔茨堡大学）
LawSum: A weakly supervised approach for Indian Legal Document Summarization
Dhirubhai Ambani Institute of Information and Communication Technology（迪鲁巴伊·安巴尼信息与通信技术学院）；Malaviya National Institute of Technology（马拉维亚国家理工学院）；Paramonic AI
When Large Language Models Meet Citation: A Survey
Macquarie University（麦考瑞大学）；Wuhan University（武汉大学）；Nanyang Technological University（南洋理工大学）；CSIRO’s Data61（澳大利亚联邦科学与工业研究组织Data61）；University of New South Wales（新南威尔士大学）；The University of Adelaide（阿德莱德大学）
