Papers by Hengzhu Tang

2 papers
Neural Extractive Summarization with Hierarchical Attentive Heterogeneous Graph Network (2020.emnlp-main)

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Challenge: Existing extractive summarization methods focus on balancing salience and redundancy between sentences.
Approach: They propose a hierarchical attentive heterogeneous graph for text summarization that models sentences . they propose to iteratively refine the sentence representations and deliver the labels by message passing .
Outcome: The proposed method outperforms existing extractive summarization methods on large corpus.
Document-level Relation Extraction with Dual-tier Heterogeneous Graph (2020.coling-main)

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Challenge: Existing methods focus on extracting relations from single sentence . document-level relation extraction requires a comprehension of the whole document .
Approach: They propose a graph-based model with Dual-tier Heterogeneous Graph (DHG) for document-level relation extraction.
Outcome: The proposed model achieves state-of-the-art performance on two widely used datasets.

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