Structured Summary
Structured summarization research focuses on automatically generating concise and informative summaries from various text sources, prioritizing factual accuracy and coherence. Current efforts concentrate on improving the faithfulness and informativeness of Large Language Models (LLMs) for summarization, addressing issues like hallucination and bias, and developing more robust evaluation metrics beyond simple overlap measures. This field is crucial for efficiently managing the ever-increasing volume of digital information, with applications ranging from healthcare and finance to scientific literature review and improved accessibility of information. The development of more effective summarization techniques is driving advancements in both LLM architecture and evaluation methodologies.
Papers
Unlocking Legal Knowledge: A Multilingual Dataset for Judicial Summarization in Switzerland
Luca Rolshoven, Vishvaksenan Rasiah, Srinanda Brügger Bose, Matthias Stürmer, Joel Niklaus
FaithBench: A Diverse Hallucination Benchmark for Summarization by Modern LLMs
Forrest Sheng Bao, Miaoran Li, Renyi Qu, Ge Luo, Erana Wan, Yujia Tang, Weisi Fan, Manveer Singh Tamber, Suleman Kazi, Vivek Sourabh, Mike Qi, Ruixuan Tu, Chenyu Xu, Matthew Gonzales, Ofer Mendelevitch, Amin Ahmad