Congbo Ma


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Incorporating Linguistic Knowledge for Abstractive Multi-document Summarization
Congbo Ma | Wei Emma Zhang | Hu Wang | Shubham Gupta | Mingyu Guo
Proceedings of the 36th Pacific Asia Conference on Language, Information and Computation

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An Empirical Study on Topic Preservation in Multi-Document Summarization
Mong Yuan Sim | Wei Emma Zhang | Congbo Ma
Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing: Student Research Workshop

Multi-document summarization (MDS) is a process of generating an informative and concise summary from multiple topic-related documents. Many studies have analyzed the quality of MDS dataset or models, however no work has been done from the perspective of topic preservation. In this work, we fill the gap by performing an empirical analysis on two MDS datasets and study topic preservation on generated summaries from 8 MDS models.Our key findings include i) Multi-News dataset has better gold summaries compared to Multi-XScience in terms of its topic distribution consistency and ii) Extractive approaches perform better than abstractive approaches in preserving topic information from source documents. We hope our findings could help develop a summarization model that can generate topic-focused summary and also give inspiration to researchers in creating dataset for such challenging task.

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Learning From the Source Document: Unsupervised Abstractive Summarization
Haojie Zhuang | Wei Emma Zhang | Jian Yang | Congbo Ma | Yutong Qu | Quan Z. Sheng
Findings of the Association for Computational Linguistics: EMNLP 2022

Most of the state-of-the-art methods for abstractive text summarization are under supervised learning settings, while heavily relying on high-quality and large-scale parallel corpora. In this paper, we remove the need for reference summaries and present an unsupervised learning method SCR (Summarize, Contrast and Review) for abstractive summarization, which leverages contrastive learning and is the first work to apply contrastive learning for unsupervised abstractive summarization. Particularly, we use the true source documents as positive source document examples, and strategically generated fake source documents as negative source document examples to train the model to generate good summaries. Furthermore, we consider and improve the writing quality of the generated summaries by guiding them to be similar to human-written texts. The promising results on extensive experiments show that SCR outperforms other unsupervised abstractive summarization baselines, which demonstrates its effectiveness.