Ziqiang Cao


2021

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BASS: Boosting Abstractive Summarization with Unified Semantic Graph
Wenhao Wu | Wei Li | Xinyan Xiao | Jiachen Liu | Ziqiang Cao | Sujian Li | Hua Wu | Haifeng Wang
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Abstractive summarization for long-document or multi-document remains challenging for the Seq2Seq architecture, as Seq2Seq is not good at analyzing long-distance relations in text. In this paper, we present BASS, a novel framework for Boosting Abstractive Summarization based on a unified Semantic graph, which aggregates co-referent phrases distributing across a long range of context and conveys rich relations between phrases. Further, a graph-based encoder-decoder model is proposed to improve both the document representation and summary generation process by leveraging the graph structure. Specifically, several graph augmentation methods are designed to encode both the explicit and implicit relations in the text while the graph-propagation attention mechanism is developed in the decoder to select salient content into the summary. Empirical results show that the proposed architecture brings substantial improvements for both long-document and multi-document summarization tasks.

2018

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Retrieve, Rerank and Rewrite: Soft Template Based Neural Summarization
Ziqiang Cao | Wenjie Li | Sujian Li | Furu Wei
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Most previous seq2seq summarization systems purely depend on the source text to generate summaries, which tends to work unstably. Inspired by the traditional template-based summarization approaches, this paper proposes to use existing summaries as soft templates to guide the seq2seq model. To this end, we use a popular IR platform to Retrieve proper summaries as candidate templates. Then, we extend the seq2seq framework to jointly conduct template Reranking and template-aware summary generation (Rewriting). Experiments show that, in terms of informativeness, our model significantly outperforms the state-of-the-art methods, and even soft templates themselves demonstrate high competitiveness. In addition, the import of high-quality external summaries improves the stability and readability of generated summaries.

2017

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DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset
Yanran Li | Hui Su | Xiaoyu Shen | Wenjie Li | Ziqiang Cao | Shuzi Niu
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

We develop a high-quality multi-turn dialog dataset, DailyDialog, which is intriguing in several aspects. The language is human-written and less noisy. The dialogues in the dataset reflect our daily communication way and cover various topics about our daily life. We also manually label the developed dataset with communication intention and emotion information. Then, we evaluate existing approaches on DailyDialog dataset and hope it benefit the research field of dialog systems. The dataset is available on http://yanran.li/dailydialog

2016

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AttSum: Joint Learning of Focusing and Summarization with Neural Attention
Ziqiang Cao | Wenjie Li | Sujian Li | Furu Wei | Yanran Li
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Query relevance ranking and sentence saliency ranking are the two main tasks in extractive query-focused summarization. Previous supervised summarization systems often perform the two tasks in isolation. However, since reference summaries are the trade-off between relevance and saliency, using them as supervision, neither of the two rankers could be trained well. This paper proposes a novel summarization system called AttSum, which tackles the two tasks jointly. It automatically learns distributed representations for sentences as well as the document cluster. Meanwhile, it applies the attention mechanism to simulate the attentive reading of human behavior when a query is given. Extensive experiments are conducted on DUC query-focused summarization benchmark datasets. Without using any hand-crafted features, AttSum achieves competitive performance. We also observe that the sentences recognized to focus on the query indeed meet the query need.

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PolyU at CL-SciSumm 2016
Ziqiang Cao | Wenjie Li | Dapeng Wu
Proceedings of the Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries (BIRNDL)

2015

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Learning Summary Prior Representation for Extractive Summarization
Ziqiang Cao | Furu Wei | Sujian Li | Wenjie Li | Ming Zhou | Houfeng Wang
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

2014

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Joint Learning of Chinese Words, Terms and Keywords
Ziqiang Cao | Sujian Li | Heng Ji
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Text-level Discourse Dependency Parsing
Sujian Li | Liang Wang | Ziqiang Cao | Wenjie Li
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)