| Challenge: | Existing methods for abstractive summarization are limited by the quality of the first-stage candidates. |
| Approach: | They propose a method that fuses several summary candidates to produce a novel abstractive second-stage summary. |
| Outcome: | The proposed method improves ROUGE scores and qualitative properties of fused summaries on several summarization datasets. |
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SummaReranker: A Multi-Task Mixture-of-Experts Re-ranking Framework for Abstractive Summarization (2022.acl-long)
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| Challenge: | Sequence-to-sequence neural networks have enabled great progress in abstractive summarization. |
| Approach: | They propose to train a second-stage model performing re-ranking on a set of summary candidates by using a mixture of experts. |
| Outcome: | The proposed model outperforms the base model on CNN- DailyMail, XSum and Reddit TIFU with a base PEGASUS. |
Balancing Lexical and Semantic Quality in Abstractive Summarization (2023.acl-short)
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| Challenge: | Existing methods to reduce exposure bias in sequence-to-sequence models are underexplored. |
| Approach: | They propose a method to re-rank sequence-to-sequence neural models to reduce exposure bias. |
| Outcome: | The proposed method achieves an 89.67 BERTScore on the CNN/DailyMail and XSum datasets. |
A New Approach to Overgenerating and Scoring Abstractive Summaries (2021.naacl-main)
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| Challenge: | Abstractive summarization is a learning objective to produce system outputs that resemble reference summaries on a word-to-word basis. |
| Approach: | They propose a two-staged strategy to generate multiple variants of the target summary and score and select admissible ones according to users’ needs. |
| Outcome: | The proposed approach can achieve state-of-the-art on benchmark summarization datasets. |
A Cascade Approach to Neural Abstractive Summarization with Content Selection and Fusion (2020.aacl-main)
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| Challenge: | Existing systems that perform content selection and surface realization are not able to provide sufficient training data for news summarization. |
| Approach: | They propose to use a cascade architecture to perform content selection and surface realization together to generate abstracts. |
| Outcome: | The proposed architecture outperforms or outranks existing systems in terms of content selection and surface realization. |
Searching for Effective Neural Extractive Summarization: What Works and What’s Next (P19-1)
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| Challenge: | Recent years have seen success in the use of deep neural networks on text summarization, but there is no clear understanding of why they perform so well or how they might be improved. |
| Approach: | They propose to use different types of model architectures to improve extractive summarization systems. |
| Outcome: | The proposed framework achieves state-of-the-art on CNN/DailyMail by a large margin based on observations and analysis. |
Abstractive Summarizers are Excellent Extractive Summarizers (2023.acl-short)
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| Challenge: | Abstractive summarization systems have traditionally been fragmented, limiting the benefits of compatible models. |
| Approach: | They propose three new inference algorithms using sequence-to-sequence architectures to model extractive summarization with an abstractive summmarization system. |
| Outcome: | The proposed algorithms outperform existing models on CNN and Dailymail and show that they are more efficient than existing models. |
Unsupervised Summarization Re-ranking (2023.findings-acl)
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| Challenge: | Abstractive summarization models have been gaining popularity, but performance of unsupervised models still lags behind supervised models. |
| Approach: | They propose to re-rank summary candidates in an unsupervised manner to close the performance gap between unsupervised and supervised models. |
| Outcome: | The proposed model improves unsupervised models by up to 7.27% and ChatGPT by up 6.86% relative mean ROUGE across four widely-adopted summarization benchmarks. |
Bottom-Up Abstractive Summarization (D18-1)
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| Challenge: | Existing approaches to summarize text using end-to-end content selectors have had mixed success in content selection, for example copying full sentences from the source document. |
| Approach: | They propose to use content selectors to over-determine phrases in a source document that should be part of the summary. |
| Outcome: | The proposed model over-determines phrases in a source document that should be part of the summary while generating fluent summaries. |
What Have We Achieved on Text Summarization? (2020.emnlp-main)
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| Challenge: | Existing methods for text summarization have been investigated, but there are still gaps between them and human professionals. |
| Approach: | They analyze 8 major sources of errors on 10 representative summarization models manually. |
| Outcome: | Aiming to gain more understanding of summarization systems with respect to their strengths and limitations on a fine-grained syntactic and semantic level, we use 8 major sources of errors on 10 representative summarizing models. |
Neural Extractive Text Summarization with Syntactic Compression (D19-1)
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| Challenge: | Recent approaches to summarization are either selection-based extraction or generation-based abstraction. |
| Approach: | They propose a neural model for single-document summarization based on joint extraction and syntactic compression. |
| Outcome: | The proposed model outperforms an off-the-shelf compression module and its output generally remains grammatical. |