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Jin-ge Yao

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3 papers

1 author row

AAAI Conference 2017 Conference Paper

Greedy Flipping for Constrained Word Deletion

Jin-ge Yao
Xiaojun Wan

In this paper we propose a simple yet efﬁcient method for constrained word deletion to compress sentences, based on top-down greedy local ﬂipping from multiple random initializations. The algorithm naturally integrates various grammatical constraints in the compression process, without using time-consuming integer linear programming solvers. Our formulation suits for any objective function involving arbitrary local score deﬁnition. Experimental results show that the proposed method achieves nearly identical performance with explicit ILP formulation while being much more efﬁcient.

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AAAI Conference 2016 Conference Paper

Tweet Timeline Generation with Determinantal Point Processes

Jin-ge Yao
Feifan Fan
Wayne Xin Zhao
Xiaojun Wan
Edward Chang
Jianguo Xiao

The task of tweet timeline generation (TTG) aims at selecting a small set of representative tweets to generate a meaningful timeline and providing enough coverage for a given topical query. This paper presents an approach based on determinantal point processes (DPPs) by jointly modeling the topical relevance of each selected tweet and overall selectional diversity. Aiming at better treatment for balancing relevance and diversity, we introduce two novel strategies, namely spectral rescaling and topical prior. Extensive experiments on the public TREC 2014 dataset demonstrate that our proposed DPP model along with the two strategies can achieve fairly competitive results against the state-of-the-art TTG systems.

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IJCAI Conference 2015 Conference Paper

Compressive Document Summarization via Sparse Optimization

Jin-ge Yao
Xiaojun Wan
Jianguo Xiao

In this paper, we formulate a sparse optimization framework for extractive document summarization. The proposed framework has a decomposable convex objective function. We derive an efficient ADMM algorithm to solve it. To encourage diversity in the summaries, we explicitly introduce an additional sentence dissimilarity term in the optimization framework. We achieve significant improvement over previous related work under similar data reconstruction framework. We then generalize our formulation to the case of compressive summarization and derive a block coordinate descent algorithm to optimize the objective function. Performance on DUC 2006 and DUC 2007 datasets shows that our compressive summarization results are competitive against the state-of-the-art results while maintaining reasonable readability.

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