Papers with abstractive document summarization
On Faithfulness and Factuality in Abstractive Summarization (2020.acl-main)
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| Challenge: | Existing conditional text generation models produce unfaithful and unfaithed summaries . current models accomplish a high level of fluency and coherence . |
| Approach: | They propose to use pretrained models for document summarization to better understand hallucinations . they find that textual entailment measures better correlate with faithfulness . |
| Outcome: | The proposed models generate faithful and factual summaries as evaluated by humans. |
Improving Abstractive Document Summarization with Salient Information Modeling (P19-1)
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| Challenge: | Abstractive document summarization is a task of natural language generation which generates fluent summaries with salient information automatically. |
| Approach: | They propose to incorporate a Gaussian focal bias on attention scores into an encoder to enhance the perception of local context and to distinguish salient information precisely. |
| Outcome: | The proposed framework outperforms state-of-the-art models on the CNN/Daily Mail benchmark and is based on a focus-attention mechanism and two new extensions. |
Improving Neural Abstractive Document Summarization with Explicit Information Selection Modeling (D18-1)
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| Challenge: | Existing neural abstractive methods for document summarization are not effective for document summary. |
| Approach: | They propose to extend basic neural encoding-decoding framework with an information selection layer to explicitly model and optimize the information selection process in abstractive document summarization. |
| Outcome: | The proposed model outperforms state-of-the-art methods on document summarization tasks significantly. |
Towards Summarizing Healthcare Questions in Low-Resource Setting (2022.coling-1)
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| Challenge: | Existing methods to generate large-scale datasets are difficult in closed domains where human annotation requires domain expertise. |
| Approach: | They propose a method to generate diverse and semantic questions in a low-resource setting with the aim of summarizing healthcare questions. |
| Outcome: | The proposed method generates diverse, fluent, and informative summarized questions on healthcare question summarization datasets. |
Evaluating and Improving Factuality in Multimodal Abstractive Summarization (2022.emnlp-main)
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| Challenge: | Current factuality metrics do not account for vision modality, thus are not adequate for vision-and-language summarization. |
| Approach: | They propose a weighted combination of CLIPScore and BERTScore to evaluate factuality for abstractive document summarization. |
| Outcome: | The proposed metric outperforms existing factuality metrics on four factuity metric-evaluation benchmarks and is robust to human judgments. |