| Challenge: | Existing empirical summarization frameworks only identify signals correlating with the vague human intuition of Importance. |
| Approach: | They propose to define several concepts that were previously used only intuitively in summarization: redundancy, Relevance, and Informativeness. |
| Outcome: | The proposed models will improve summarization systems and improve their performance on standard datasets, while the notion of Importance remains latent. |
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| Challenge: | Existing studies on content importance do not consider semantics and context when evaluating importance. |
| Approach: | They apply information theory to pre-trained language models to define the concept of importance from the perspective of information amount. |
| Outcome: | Experiments on CNN/Daily Mail and New York Times show that the proposed model can model the importance of content better than previous methods based on F1 and ROUGE scores. |
Which Information Matters? Dissecting Human-written Multi-document Summaries with Partial Information Decomposition (2024.findings-acl)
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| Challenge: | Existing methods to characterize human-written summaries do not account for the nature of high-quality summary. |
| Approach: | They propose to characterize human-written summaries using partial information decomposition . they propose to decompose mutual information provided by all source documents into union, redundancy, synergy, and unique information . |
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Systematically Exploring Redundancy Reduction in Summarizing Long Documents (2020.aacl-main)
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| Challenge: | Summarization tasks are often based on importance and diversity, but there is a trade-off between importance and non-redundancy. |
| Approach: | They propose to organize existing methods into categories based on when and how redundancy is considered and propose three additional methods balancing non-redundancy and importance in a general and flexible way. |
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Summarizing Text on Any Aspects: A Knowledge-Informed Weakly-Supervised Approach (2020.emnlp-main)
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| Challenge: | Existing studies on aspect-based abstractive summarization assume a small set of aspects and do not consider other diverse aspects. |
| Approach: | They propose a weak supervision construction method and an aspect modeling scheme to solve this problem. |
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Proceedings of the 2nd Workshop on New Frontiers in Summarization (D19-54)
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| Challenge: | EMNLP 2017 is a workshop on enhancing natural language processing's ability to produce concise, fluent summaries. |
| Approach: | the workshop provides a forum for cross-fertilization of ideas towards automatic summarization . four invited speakers will be present at the workshop . |
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How well do you know your summarization datasets? (2021.findings-acl)
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| Challenge: | State-of-the-art summarization systems are trained on massive datasets scraped from the web. |
| Approach: | They manually analyse 600 samples from three popular summarization datasets . they use a six-class typology which captures different noise types and degrees of summarizing difficulty. |
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Controllable Text Summarization: Unraveling Challenges, Approaches, and Prospects - A Survey (2024.findings-acl)
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| Challenge: | scholarly attention has turned to the development of text summarization methods that are more closely tailored and controlled to align with specific objectives and user needs. |
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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. |
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On Importance Sampling-Based Evaluation of Latent Language Models (2020.acl-main)
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| Challenge: | Existing approaches to evaluate language models using latent structures are intractable as they require marginalizing over the latent space. |
| Approach: | They propose to use importance sampling to evaluate latent language models . they elucidate subtle differences in how importance sampling is applied . |
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PreSumm: Predicting Summarization Performance Without Summarizing (2025.findings-acl)
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| Challenge: | Recent advances in summarization models do not produce all documents in the same way, despite their inherent design principles and operational mechanisms. |
| Approach: | They propose a task where a system predicts summarization performance based solely on the source document. |
| Outcome: | The proposed task identifies documents that require manual summarization and improves dataset quality by filtering outliers and noisy documents. |