SALAS: Supervised Aspect Learning Improves Abstractive Multi-Document Summarization through Aspect Information Loss

9月 1, 2023·
Haotian Chen
Haotian Chen
,
Han Zhang
,
Houjing Guo
,
Shuchang Yi
,
Bingsheng Chen
,
Xiangdong Zhou
· 0 分钟阅读时长
摘要
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.
类型
出版物
In Machine Learning and Knowledge Discovery in Databases (ECML-PKDD 2023)
publications
Haotian Chen
Authors
Research Assistant Professor
I am a Research Assistant Professor at the School of Artificial Intelligence, Shanghai Jiao Tong University, where I work with Prof. Junchi Yan at RethinkLab. I study how to build AI systems that can automate long-horizon, effort-intensive, and creativity-demanding tasks such as research, engineering, and development. My current work focuses on autonomous agents, large language models, and AI4Research. Before joining SJTU, I received my PhD in Data Science from Fudan University, advised by Prof. Xiangdong Zhou, and completed postdoctoral research at Tsinghua University (THUNLP), working with Prof. Zhiyuan Liu and Prof. Maosong Sun. I was also a research intern at the Machine Learning Research Group of Microsoft Research Asia, mentored by Xiao Yang, and at the Institute for Interdisciplinary Information Sciences (IIIS), Tsinghua University, working with Prof. Yang Yu.