Papers with neural abstractive summarization
Global Encoding for Abstractive Summarization (P18-2)
Copied to clipboard
| Challenge: | Existing models for abstractive summarization suffer from repetition and semantic irrelevance. |
| Approach: | They propose a global encoding framework which controls the information flow from the encoder to the decoder based on the global information of the source context. |
| Outcome: | The proposed model outperforms baseline models on the LCSTS and English Gigaword and can generate summary of higher quality and reduce repetition. |
Using Question Answering Rewards to Improve Abstractive Summarization (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Neural abstractive summarization models have seen improvements in recent years, but they still suffer from multiple drawbacks. |
| Approach: | They propose a general framework to train abstractive summarization models to alleviate these issues by question-answering based rewards. |
| Outcome: | The proposed framework is preferred over general abstractive summarization models. |
Keeping Consistency of Sentence Generation and Document Classification with Multi-Task Learning (D19-1)
Copied to clipboard
| Challenge: | Existing automated generation of articles' characteristics is inconsistent if they are generated individually. |
| Approach: | They propose a multi-task learning model with a shared encoder and multiple decoders for each task. |
| Outcome: | The proposed model generates more consistent headlines, key phrases and categories . it outperforms baseline model on the ROUGE scores and generates fluent headlines . |
Improving Faithfulness in Abstractive Summarization with Contrast Candidate Generation and Selection (2021.naacl-main)
Copied to clipboard
| Challenge: | Recent studies have shown that current models are prone to generating unfaithful summaries . a proposed method is effective in identifying and correcting extrinsic hallucinations . |
| Approach: | They propose a model-agnostic post-processing technique to correct unfaithful summaries . they generate alternative candidates where names and quantities are replaced with compatible ones . |
| Outcome: | The proposed method corrects extrinsic hallucinations in unfaithful summaries. |