Ming Zhong


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CoLo: A Contrastive Learning Based Re-ranking Framework for One-Stage Summarization
Chenxin An | Ming Zhong | Zhiyong Wu | Qin Zhu | Xuanjing Huang | Xipeng Qiu
Proceedings of the 29th International Conference on Computational Linguistics

Traditional training paradigms for extractive and abstractive summarization systems always only use token-level or sentence-level training objectives. However, the output summary is always evaluated from summary-level which leads to the inconsistency in training and evaluation. In this paper, we propose a Contrastive Learning based re-ranking framework for one-stage summarization called CoLo. By modeling a contrastive objective, we show that the summarization model is able to directly generate summaries according to the summary-level score without additional modules and parameters. Extensive experiments demonstrate that CoLo boosts the extractive and abstractive results of one-stage systems on CNN/DailyMail benchmark to 44.58 and 46.33 ROUGE-1 score while preserving the parameter efficiency and inference efficiency. Compared with state-of-the-art multi-stage systems, we save more than 100 GPU training hours and obtaining 3x 8x speed-up ratio during inference while maintaining comparable results.

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Improving Abstractive Dialogue Summarization with Speaker-Aware Supervised Contrastive Learning
Zhichao Geng | Ming Zhong | Zhangyue Yin | Xipeng Qiu | Xuanjing Huang
Proceedings of the 29th International Conference on Computational Linguistics

Pre-trained models have brought remarkable success on the text summarization task. For dialogue summarization, the subdomain of text summarization, utterances are concatenated to flat text before being processed. As a result, existing summarization systems based on pre-trained models are unable to recognize the unique format of the speaker-utterance pair well in the dialogue. To investigate this issue, we conduct probing tests and manual analysis, and find that the powerful pre-trained model can not identify different speakers well in the conversation, which leads to various factual errors. Moreover, we propose three speaker-aware supervised contrastive learning (SCL) tasks: Token-level SCL, Turn-level SCL, and Global-level SCL. Comprehensive experiments demonstrate that our methods achieve significant performance improvement on two mainstream dialogue summarization datasets. According to detailed human evaluations, pre-trained models equipped with SCL tasks effectively generate summaries with better factual consistency.


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GEM: A General Evaluation Benchmark for Multimodal Tasks
Lin Su | Nan Duan | Edward Cui | Lei Ji | Chenfei Wu | Huaishao Luo | Yongfei Liu | Ming Zhong | Taroon Bharti | Arun Sacheti
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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QMSum: A New Benchmark for Query-based Multi-domain Meeting Summarization
Ming Zhong | Da Yin | Tao Yu | Ahmad Zaidi | Mutethia Mutuma | Rahul Jha | Ahmed Hassan Awadallah | Asli Celikyilmaz | Yang Liu | Xipeng Qiu | Dragomir Radev
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Meetings are a key component of human collaboration. As increasing numbers of meetings are recorded and transcribed, meeting summaries have become essential to remind those who may or may not have attended the meetings about the key decisions made and the tasks to be completed. However, it is hard to create a single short summary that covers all the content of a long meeting involving multiple people and topics. In order to satisfy the needs of different types of users, we define a new query-based multi-domain meeting summarization task, where models have to select and summarize relevant spans of meetings in response to a query, and we introduce QMSum, a new benchmark for this task. QMSum consists of 1,808 query-summary pairs over 232 meetings in multiple domains. Besides, we investigate a locate-then-summarize method and evaluate a set of strong summarization baselines on the task. Experimental results and manual analysis reveal that QMSum presents significant challenges in long meeting summarization for future research. Dataset is available at https://github.com/Yale-LILY/QMSum.


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Extractive Summarization as Text Matching
Ming Zhong | Pengfei Liu | Yiran Chen | Danqing Wang | Xipeng Qiu | Xuanjing Huang
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

This paper creates a paradigm shift with regard to the way we build neural extractive summarization systems. Instead of following the commonly used framework of extracting sentences individually and modeling the relationship between sentences, we formulate the extractive summarization task as a semantic text matching problem, in which a source document and candidate summaries will be (extracted from the original text) matched in a semantic space. Notably, this paradigm shift to semantic matching framework is well-grounded in our comprehensive analysis of the inherent gap between sentence-level and summary-level extractors based on the property of the dataset. Besides, even instantiating the framework with a simple form of a matching model, we have driven the state-of-the-art extractive result on CNN/DailyMail to a new level (44.41 in ROUGE-1). Experiments on the other five datasets also show the effectiveness of the matching framework. We believe the power of this matching-based summarization framework has not been fully exploited. To encourage more instantiations in the future, we have released our codes, processed dataset, as well as generated summaries in https://github.com/maszhongming/MatchSum.

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CDEvalSumm: An Empirical Study of Cross-Dataset Evaluation for Neural Summarization Systems
Yiran Chen | Pengfei Liu | Ming Zhong | Zi-Yi Dou | Danqing Wang | Xipeng Qiu | Xuanjing Huang
Findings of the Association for Computational Linguistics: EMNLP 2020

Neural network-based models augmented with unsupervised pre-trained knowledge have achieved impressive performance on text summarization. However, most existing evaluation methods are limited to an in-domain setting, where summarizers are trained and evaluated on the same dataset. We argue that this approach can narrow our understanding of the generalization ability for different summarization systems. In this paper, we perform an in-depth analysis of characteristics of different datasets and investigate the performance of different summarization models under a cross-dataset setting, in which a summarizer trained on one corpus will be evaluated on a range of out-of-domain corpora. A comprehensive study of 11 representative summarization systems on 5 datasets from different domains reveals the effect of model architectures and generation ways (i.e. abstractive and extractive) on model generalization ability. Further, experimental results shed light on the limitations of existing summarizers. Brief introduction and supplementary code can be found in https://github.com/zide05/CDEvalSumm.


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A Closer Look at Data Bias in Neural Extractive Summarization Models
Ming Zhong | Danqing Wang | Pengfei Liu | Xipeng Qiu | Xuanjing Huang
Proceedings of the 2nd Workshop on New Frontiers in Summarization

In this paper, we take stock of the current state of summarization datasets and explore how different factors of datasets influence the generalization behaviour of neural extractive summarization models. Specifically, we first propose several properties of datasets, which matter for the generalization of summarization models. Then we build the connection between priors residing in datasets and model designs, analyzing how different properties of datasets influence the choices of model structure design and training methods. Finally, by taking a typical dataset as an example, we rethink the process of the model design based on the experience of the above analysis. We demonstrate that when we have a deep understanding of the characteristics of datasets, a simple approach can bring significant improvements to the existing state-of-the-art model.

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Searching for Effective Neural Extractive Summarization: What Works and What’s Next
Ming Zhong | Pengfei Liu | Danqing Wang | Xipeng Qiu | Xuanjing Huang
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

The recent years have seen remarkable success in the use of deep neural networks on text summarization. However, there is no clear understanding of why they perform so well, or how they might be improved. In this paper, we seek to better understand how neural extractive summarization systems could benefit from different types of model architectures, transferable knowledge and learning schemas. Besides, we find an effective way to improve the current framework and achieve the state-of-the-art result on CNN/DailyMail by a large margin based on our observations and analysis. Hopefully, our work could provide more hints for future research on extractive summarization.