| Challenge: | Existing approaches to extract summarize text are based on sentences as the elementary unit, but semantic segments containing supplementary information or descriptive details are often nonessential in the generated summaries. |
| Approach: | They propose to exploit discourse-level segmentation as a finer-grained means to more precisely pinpoint the core content in a document. |
| Outcome: | The proposed method improves extractive summarization performance on CNN/Daily Mail dataset. |
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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. |
At Which Level Should We Extract? An Empirical Analysis on Extractive Document Summarization (2020.coling-main)
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| Challenge: | Existing studies have shown that extracting sentences at sentence level is not the best solution for document summarization. |
| Approach: | They propose to extract sub-sentential units based on the constituency parsing tree and a neural extractive model which leverages the sub-sensential information and extracts them. |
| Outcome: | The proposed model performs competitively compared to full sentence extraction under automatic and human evaluations. |
Toward Unifying Text Segmentation and Long Document Summarization (2022.emnlp-main)
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| Challenge: | Abstractive strategies produce more condensed summaries, but they suffer from hallucinations and factual errors, which pose a more difficult generation challenge. |
| Approach: | They propose a method that learns robust sentence representations by performing summarization and segmentation simultaneously, which is further enhanced by an optimization-based regularizer to promote selection of diverse summary sentences. |
| Outcome: | The proposed model achieves state-of-the-art performance on publicly available benchmarks and better cross-genre transferability when equipped with text segmentation. |
Extractive Summarization as Text Matching (2020.acl-main)
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| Challenge: | Currently, most of the neural extractive summarization systems score and extract sentences individually and model the relationship between sentences. |
| Approach: | They propose to instantiate a neural extractive summarization task as a semantic text matching problem and use it to match a source document and candidate summaries in a semantic space. |
| Outcome: | The proposed framework is faster and more efficient than existing frameworks. |
Frame Semantic-Enhanced Sentence Modeling for Sentence-level Extractive Text Summarization (2021.emnlp-main)
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| Challenge: | Sentence-level extractive text summarization is difficult to model the importance of sentences. |
| Approach: | They propose a Frame Semantic-Enhanced Sentence Modeling for Extractive Summarization that leverages Frame semantics to model sentences from both intra-sentence level and inter-sentent level. |
| Outcome: | The proposed model outperforms six state-of-the-art methods on two benchmark corpus datasets. |
Discourse-Aware Neural Extractive Text Summarization (2020.acl-main)
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| Challenge: | Recent studies have shown that sentence-based extractive models result in redundant or uninformative phrases in the extracted summaries. |
| Approach: | They propose a discourse-aware neural summarization model that extracts sub-sentential discourse units as candidates for extractive selection on a finer granularity. |
| Outcome: | Experiments show that the proposed model outperforms state-of-the-art models on popular summarization benchmarks. |
Enriching and Controlling Global Semantics for Text Summarization (2021.emnlp-main)
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| Challenge: | Abstractive summarization models have been proven effective in creating fluent and informative summaries, but they suffer from the short-range dependency problem, causing them to produce summary that miss the key points of document. |
| Approach: | They propose a neural topic model empowered with normalizing flow to capture global semantics of the document and integrate them into the summarization model. |
| Outcome: | The proposed model outperforms state-of-the-art summarization models on five common text summarizing datasets, namely CNN/DailyMail, XSum, Reddit TIFU, arXiv, and PubMed. |
Unsupervised Extractive Summarization by Pre-training Hierarchical Transformers (2020.findings-emnlp)
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| Challenge: | Existing methods for document summarization use graphs and unlabeled documents . Existing models require labeled data, and it is expensive to create summarized documents. |
| Approach: | They propose to rank sentences using transformer attentions and pre-training objectives by unlabeled documents. |
| Outcome: | The proposed model achieves state-of-the-art on unsupervised summarization and is less dependent on sentence positions. |
Enhancing Extractive Text Summarization with Topic-Aware Graph Neural Networks (2020.coling-main)
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| Challenge: | Existing extractive summarization models hardly capture inter-sentence relationships, especially in long documents. |
| Approach: | They propose to use a graph neural network to capture inter-sentence relationships efficiently via graph-structured document representation. |
| Outcome: | The proposed model outperforms existing models on CNN/DM and NYT datasets and significantly outperfies them on longer documents. |
Deep Differential Amplifier for Extractive Summarization (2021.acl-long)
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| Challenge: | Existing approaches to extract summary from document with a disproportionate ratio of selected and unselected sentences are far from human performance. |
| Approach: | They propose a model that rebalances sentence-level extractive summarization by amplifying the semantic difference between each sentence and all other sentences and applying the residual unit as the second item of the differential amplifier to deepen the architecture. |
| Outcome: | The proposed model performs competitively against state-of-the-art methods on two benchmark datasets. |