Papers by Hanqi Jin
Multi-Granularity Interaction Network for Extractive and Abstractive Multi-Document Summarization (2020.acl-main)
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| Challenge: | Existing methods for document summarization use extractive and abstractive representations, but they don't take into account hierarchical structure of document clusters. |
| Approach: | They propose a multi-granularity interaction network for extractive and abstractive multi-document summarization which jointly learn semantic representations for words, sentences, and documents. |
| Outcome: | The proposed model outperforms baseline methods and achieves the best results on the Multi-News dataset. |
Abstractive Multi-Document Summarization via Joint Learning with Single-Document Summarization (2020.findings-emnlp)
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| Challenge: | Existing methods for document summarization are extractive and abstractive. |
| Approach: | They propose to jointly learn an abstractive single-document decoder and a decoding controller to aggregate the decoded outputs for multiple input documents. |
| Outcome: | The proposed model outperforms several baselines on two multi-document summarization datasets and proves that it is useful for both tasks. |
Document-Level Text Simplification: Dataset, Criteria and Baseline (2021.emnlp-main)
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| Challenge: | Text simplification is a valuable technique, but research on it is limited. |
| Approach: | They propose a document-level simplification task using Wikipedia dumps as a dataset and propose an automatic evaluation metric called D-SARI. |
| Outcome: | The proposed metric is more suitable for document-level simplification task. |
AMR-To-Text Generation with Graph Transformer (2020.tacl-1)
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| Challenge: | Abstract meaning representation (AMR)-to-text generation is challenging task for natural language processing. |
| Approach: | They propose a graph-to-sequence model that directly encodes AMR graphs and learns node representations. |
| Outcome: | The proposed model outperforms the current state-of-the-art neural approach by 1.5 BLEU points on LDC2015E86 and 4.8 BLUE points on the LDC2017T10 and achieves new state- of-the art performance. |