| Challenge: | Existing summarization methods define relevance based on textual information alone without incorporating insights about a particular decision. |
| Approach: | They propose a method that summarizes relevant information for a decision using full text . they then build a model that makes the decision based on the full text while accounting for textual non-redundancy. |
| Outcome: | The proposed method outperforms text-only summarization methods and model-based explanation methods in decision faithfulness and representativeness. |
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| Challenge: | Abstractive summarization models have made great strides in recent years, but little is known about how they actually form summaries and how to understand where their decisions come from. |
| Approach: | They propose a two-step method to interpret summarization model decisions by categorizing each decoder decision into one of several generation modes. |
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Effective Crowdsourcing for a New Type of Summarization Task (N18-2)
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| Challenge: | Currently, summarization research focuses on summarizing the entire text, but in practice, readers are often interested in only one aspect of the document or conversation. |
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Learning to Prioritize: Precision-Driven Sentence Filtering for Long Text Summarization (2022.lrec-1)
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| Challenge: | Neural text summarization models are limited by their maximum input length, posing a challenge to summarizing longer texts comprehensively. |
| Approach: | They propose a pre-processing layer that removes low-quality sentences in articles to improve existing summarization models. |
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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. |
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Extractive Summarization of Legal Decisions using Multi-task Learning and Maximal Marginal Relevance (2022.findings-emnlp)
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| Challenge: | Summarizing legal decisions requires the expertise of law practitioners, which is time- and cost-intensive. |
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Summarization Evaluation in the Absence of Human Model Summaries Using the Compositionality of Word Embeddings (C18-1)
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| Challenge: | Existing summary evaluation methods rely on multiple model summaries to evaluate quality of summary outputs. |
| Approach: | They propose a new summary evaluation approach that does not require human model summaries . they exploit compositional capabilities of word embeddings to develop features . |
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Content Selection in Deep Learning Models of Summarization (D18-1)
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| Challenge: | Using deep learning models, we find that word embedding does not improve performance over simpler models. |
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SumPubMed: Summarization Dataset of PubMed Scientific Articles (2021.acl-srw)
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| Challenge: | Existing summarization models that can extract the top few lines of news articles fail to summarize long documents. |
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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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A Summarization System for Scientific Documents (D19-3)
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Shai Erera, Michal Shmueli-Scheuer, Guy Feigenblat, Ora Peled Nakash, Odellia Boni, Haggai Roitman, Doron Cohen, Bar Weiner, Yosi Mass, Or Rivlin, Guy Lev, Achiya Jerbi, Jonathan Herzig, Yufang Hou, Charles Jochim, Martin Gleize, Francesca Bonin, Francesca Bonin, David Konopnicki
| Challenge: | a qualitative user study identified the most valuable scenarios for scientific content consumption. |
| Approach: | They propose a system that retrieves and summarizes scientific documents for a given information need. |
| Outcome: | The proposed system ingested 270,000 scientific papers and validated with human experts. |