Tianming Wang


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AMR-To-Text Generation with Graph Transformer
Tianming Wang | Xiaojun Wan | Hanqi Jin
Transactions of the Association for Computational Linguistics, Volume 8

Abstract meaning representation (AMR)-to-text generation is the challenging task of generating natural language texts from AMR graphs, where nodes represent concepts and edges denote relations. The current state-of-the-art methods use graph-to-sequence models; however, they still cannot significantly outperform the previous sequence-to-sequence models or statistical approaches. In this paper, we propose a novel graph-to-sequence model (Graph Transformer) to address this task. The model directly encodes the AMR graphs and learns the node representations. A pairwise interaction function is used for computing the semantic relations between the concepts. Moreover, attention mechanisms are used for aggregating the information from the incoming and outgoing neighbors, which help the model to capture the semantic information effectively. Our model outperforms the state-of-the-art neural approach by 1.5 BLEU points on LDC2015E86 and 4.8 BLEU points on LDC2017T10 and achieves new state-of-the-art performances.

Multi-Granularity Interaction Network for Extractive and Abstractive Multi-Document Summarization
Hanqi Jin | Tianming Wang | Xiaojun Wan
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

In this paper, we propose a multi-granularity interaction network for extractive and abstractive multi-document summarization, which jointly learn semantic representations for words, sentences, and documents. The word representations are used to generate an abstractive summary while the sentence representations are used to produce an extractive summary. We employ attention mechanisms to interact between different granularity of semantic representations, which helps to capture multi-granularity key information and improves the performance of both abstractive and extractive summarization. Experiment results show that our proposed model substantially outperforms all strong baseline methods and achieves the best results on the Multi-News dataset.

Heterogeneous Graph Transformer for Graph-to-Sequence Learning
Shaowei Yao | Tianming Wang | Xiaojun Wan
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

The graph-to-sequence (Graph2Seq) learning aims to transduce graph-structured representations to word sequences for text generation. Recent studies propose various models to encode graph structure. However, most previous works ignore the indirect relations between distance nodes, or treat indirect relations and direct relations in the same way. In this paper, we propose the Heterogeneous Graph Transformer to independently model the different relations in the individual subgraphs of the original graph, including direct relations, indirect relations and multiple possible relations between nodes. Experimental results show that our model strongly outperforms the state of the art on all four standard benchmarks of AMR-to-text generation and syntax-based neural machine translation.


Automatic Labeling of Topic Models Using Text Summaries
Xiaojun Wan | Tianming Wang
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

PKUSUMSUM : A Java Platform for Multilingual Document Summarization
Jianmin Zhang | Tianming Wang | Xiaojun Wan
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: System Demonstrations

PKUSUMSUM is a Java platform for multilingual document summarization, and it sup-ports multiple languages, integrates 10 automatic summarization methods, and tackles three typical summarization tasks. The summarization platform has been released and users can easily use and update it. In this paper, we make a brief description of the char-acteristics, the summarization methods, and the evaluation results of the platform, and al-so compare PKUSUMSUM with other summarization toolkits.