Flexible and Adaptable Summarization via Expertise Separation-Reference-Cited by-同舟云学术

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Flexible and Adaptable Summarization via Expertise Separation

Published:2024-07-10 Issue: Volume: Page:2018-2027
ISSN:
Container-title:Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval
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Author:

Chen Xiuying¹^ORCID,Li Mingzhe²^ORCID,Gao Shen³^ORCID,Cheng Xin⁴^ORCID,Zhu Qingqing⁴^ORCID,Yan Rui⁵^ORCID,Gao Xin⁶^ORCID,Zhang Xiangliang⁷^ORCID

Affiliation:

1. CBRC, KAUST & MBZUAI, Jeddah, Saudi Arabia

2. Ant Group, Beijing, China

3. Shandong University, Qingdao, China

4. Peking University, Beijing, China

5. Renmin University of China, Beijing, China

6. CBRC, CEMSE, KAUST, Jeddah, Saudi Arabia

7. University of Notre Dame & KAUST, South Bend, IN, USA

Funder

King Abdullah University of Science and Technology (KAUST)

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3626772.3657789

Reference54 articles.

1. Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020. Language models are few-shot learners. Proc. of NeurIPS (2020).

2. Xiuying Chen Mingzhe Li Xin Gao and Xiangliang Zhang. 2023. Towards Improving Faithfulness in Abstractive Summarization. In Advances in Neural Information Processing Systems.

3. SPEC: Summary Preference Decomposition for Low-Resource Abstractive Summarization

4. Decouple knowledge from paramters for plug-and-play language modeling

5. A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents

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