In this paper, we introduce ChatCite, a novel method leveraging large language models (LLMs) for generating comparative literature summaries. The ability to summarize research papers with a focus on key comparisons between studies is an essential task in academic research. Existing summarization models, while effective at generating concise summaries, fail to provide deep comparative insights. ChatCite addresses this limitation by incorporating a multi-step reasoning mechanism that extracts critical elements from papers, incrementally builds a comparative summary, and refines the output through a reflective memory process. We evaluate ChatCite on a custom dataset, CompLit-LongContext, consisting of 1000 research papers with annotated comparative summaries. Experimental results show that ChatCite outperforms several baseline methods, including GPT-4, BART, T5, and CoT, across various automatic evaluation metrics such as ROUGE and the newly proposed G-Score. Human evaluation further confirms that ChatCite generates more coherent, insightful, and fluent summaries compared to these baseline models. Our method provides a significant advancement in automatic literature review generation, offering researchers a powerful tool for efficiently comparing and synthesizing scientific research.
LLM-ReSum: A Framework for LLM Reflective Summarization through Self-Evaluation
dept. of Information Science University of North Texas Denton, Texas, USA（信息科学系 俄克拉荷马州立大学 丹顿 俄克拉荷马州 美国）；dept. of Data Science University of North Texas Denton, Texas, USA（数据科学系 俄克拉荷马州立大学 丹顿 俄克拉荷马州 美国）
On Context Utilization in Summarization with Large Language Models
Nanyang Technological University（南洋理工大学）；Institute of Infocomm Research (IR), A * STAR（新加坡科技研究局信息通信研究所）；Centre for Frontier AI Research (CFAR), A*STAR（新加坡科技研究局前沿人工智能研究中心）；Salesforce Research（Salesforce 研究部）
In this work, we develop a prompting approach for incremental summarization of task videos. We develop a sample-efficient few-shot approach for extracting semantic concepts as an intermediate step. We leverage an existing model for extracting the concepts from the images and extend it to videos and introduce a clustering and querying approach for sample efficiency, motivated by the recent advances in perceiver-based architectures. Our work provides further evidence that an approach with richer input context with relevant entities and actions from the videos and using these as prompts could enh…
QRMeM: Unleash the Length Limitation through Question then Reflection Memory Mechanism
Beijing Institute of Technology（北京理工大学）；Southeast Academy of Information Technology, Beijing Institute of Technology（北京理工大学东南信息技术研究院）；Fudan University（复旦大学）；Singapore Management University（新加坡管理大学）；University of Science and Technology of China（中国科学技术大学）
