Moye Chen


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SgSum:Transforming Multi-document Summarization into Sub-graph Selection
Moye Chen | Wei Li | Jiachen Liu | Xinyan Xiao | Hua Wu | Haifeng Wang
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Most of existing extractive multi-document summarization (MDS) methods score each sentence individually and extract salient sentences one by one to compose a summary, which have two main drawbacks: (1) neglecting both the intra and cross-document relations between sentences; (2) neglecting the coherence and conciseness of the whole summary. In this paper, we propose a novel MDS framework (SgSum) to formulate the MDS task as a sub-graph selection problem, in which source documents are regarded as a relation graph of sentences (e.g., similarity graph or discourse graph) and the candidate summaries are its sub-graphs. Instead of selecting salient sentences, SgSum selects a salient sub-graph from the relation graph as the summary. Comparing with traditional methods, our method has two main advantages: (1) the relations between sentences are captured by modeling both the graph structure of the whole document set and the candidate sub-graphs; (2) directly outputs an integrate summary in the form of sub-graph which is more informative and coherent. Extensive experiments on MultiNews and DUC datasets show that our proposed method brings substantial improvements over several strong baselines. Human evaluation results also demonstrate that our model can produce significantly more coherent and informative summaries compared with traditional MDS methods. Moreover, the proposed architecture has strong transfer ability from single to multi-document input, which can reduce the resource bottleneck in MDS tasks.


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Word Embedding and Topic Modeling Enhanced Multiple Features for Content Linking and Argument / Sentiment Labeling in Online Forums
Lei Li | Liyuan Mao | Moye Chen
Proceedings of the MultiLing 2017 Workshop on Summarization and Summary Evaluation Across Source Types and Genres

Multiple grammatical and semantic features are adopted in content linking and argument/sentiment labeling for online forums in this paper. There are mainly two different methods for content linking. First, we utilize the deep feature obtained from Word Embedding Model in deep learning and compute sentence similarity. Second, we use multiple traditional features to locate candidate linking sentences, and then adopt a voting method to obtain the final result. LDA topic modeling is used to mine latent semantic feature and K-means clustering is implemented for argument labeling, while features from sentiment dictionaries and rule-based sentiment analysis are integrated for sentiment labeling. Experimental results have shown that our methods are valid.