Lei He


2016

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Learning Distributed Word Representations For Bidirectional LSTM Recurrent Neural Network
Peilu Wang | Yao Qian | Frank K. Soong | Lei He | Hai Zhao
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Abstractive News Summarization based on Event Semantic Link Network
Wei Li | Lei He | Hai Zhuge
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

This paper studies the abstractive multi-document summarization for event-oriented news texts through event information extraction and abstract representation. Fine-grained event mentions and semantic relations between them are extracted to build a unified and connected event semantic link network, an abstract representation of source texts. A network reduction algorithm is proposed to summarize the most salient and coherent event information. New sentences with good linguistic quality are automatically generated and selected through sentences over-generation and greedy-selection processes. Experimental results on DUC 2006 and DUC 2007 datasets show that our system significantly outperforms the state-of-the-art extractive and abstractive baselines under both pyramid and ROUGE evaluation metrics.

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Exploring Differential Topic Models for Comparative Summarization of Scientific Papers
Lei He | Wei Li | Hai Zhuge
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

This paper investigates differential topic models (dTM) for summarizing the differences among document groups. Starting from a simple probabilistic generative model, we propose dTM-SAGE that explicitly models the deviations on group-specific word distributions to indicate how words are used differen-tially across different document groups from a background word distribution. It is more effective to capture unique characteristics for comparing document groups. To generate dTM-based comparative summaries, we propose two sentence scoring methods for measuring the sentence discriminative capacity. Experimental results on scientific papers dataset show that our dTM-based comparative summari-zation methods significantly outperform the generic baselines and the state-of-the-art comparative summarization methods under ROUGE metrics.