SALAS: Supervised Aspect Learning Improves Abstractive Multi-Document Summarization through Aspect Information Loss
Sep 1, 2023·
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0 min read
Haotian Chen
Han Zhang
Houjing Guo
Shuchang Yi
Bingsheng Chen
Xiangdong Zhou
Abstract
We propose SALAS and a new aspect information loss (AILoss) to learn aspect information to supervise the generating process. SALAS outperforms previous SOTA baselines on three MDS datasets.
Type
Publication
In Machine Learning and Knowledge Discovery in Databases (ECML-PKDD 2023)

Authors
Haotian Chen
(he/him)
Assistant Researcher
Haotian Chen is an Assistant Researcher at the School of Artificial Intelligence, Shanghai Jiao Tong University, working with Prof. Junchi Yan at RethinkLab. His research goal is to understand and develop AI for automating tasks that require extensive time, effort, and creative thinking. He works on automating data-driven scientific research, contributing to both alleviating the burden on humans and revolutionizing human productivity. His research focuses on Autonomous Agents, Large Language Models, and AI4Research. He received his PhD in Data Science from Fudan University and completed postdoctoral research at Tsinghua University (THUNLP), where he worked with Prof. Zhiyuan Liu and Prof. Maosong Sun. He was also a research intern at Microsoft Research Asia, where the RD-Agent project he co-developed was featured in the Microsoft Build 2025 Global Keynote.