Zhuli Xie


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From Extracting to Abstracting: Generating Quasi-abstractive Summaries
Zhuli Xie | Barbara Di Eugenio | Peter C. Nelson
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

In this paper, we investigate quasi-abstractive summaries, a new type of machine-generated summaries that do not use whole sentences, but only fragments from the source. Quasi-abstractive summaries aim at bridging the gap between human-written abstracts and extractive summaries. We present an approach that learns how to identify sets of sentences, where each set contains fragments that can be used to produce one sentence in the abstract; and then uses these sets to produce the abstract itself. Our experiments show very promising results. Importantly, we obtain our best results when the summary generation is anchored by the most salient Noun Phrases predicted from the text to be summarized.


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All links are not the same: evaluating word alignments for statistical machine translation
Paul C. Davis | Zhuli Xie | Kevin Small
Proceedings of Machine Translation Summit XI: Papers


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Centrality Measures in Text Mining: Prediction of Noun Phrases that Appear in Abstracts
Zhuli Xie
Proceedings of the ACL Student Research Workshop


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Using Gene Expression Programming to Construct Sentence Ranking Functions for Text Summarization
Zhuli Xie | Xin Li | Barbara Di Eugenio | Weimin Xiao | Thomas M. Tirpak | Peter C. Nelson
COLING 2004: Proceedings of the 20th International Conference on Computational Linguistics