Shulei Ma


An Unsupervised Multi-Document Summarization Framework Based on Neural Document Model
Shulei Ma | Zhi-Hong Deng | Yunlun Yang
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

In the age of information exploding, multi-document summarization is attracting particular attention for the ability to help people get the main ideas in a short time. Traditional extractive methods simply treat the document set as a group of sentences while ignoring the global semantics of the documents. Meanwhile, neural document model is effective on representing the semantic content of documents in low-dimensional vectors. In this paper, we propose a document-level reconstruction framework named DocRebuild, which reconstructs the documents with summary sentences through a neural document model and selects summary sentences to minimize the reconstruction error. We also apply two strategies, sentence filtering and beamsearch, to improve the performance of our method. Experimental results on the benchmark datasets DUC 2006 and DUC 2007 show that DocRebuild is effective and outperforms the state-of-the-art unsupervised algorithms.

A Position Encoding Convolutional Neural Network Based on Dependency Tree for Relation Classification
Yunlun Yang | Yunhai Tong | Shulei Ma | Zhi-Hong Deng
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing