Papers with guided summarization
End-to-End Aspect-Guided Review Summarization at Scale (2025.emnlp-industry)
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Ilya Boytsov, Vinny DeGenova, Mikhail Balyasin, Joseph Walt, Caitlin Eusden, Marie-Claire Rochat, Margaret Pierson
| Challenge: | Existing methods to generate concise product review summaries are prone to hallucination, omission of important facts, and factual errors. |
| Approach: | They propose a large language model-based system that combines aspect-based sentiment analysis with guided summarization to generate concise product review summaries. |
| Outcome: | The proposed system generates concise and interpretable product review summaries using a large language model (LLM) dataset. |
Thinking about how to extract: Energizing LLMs’ emergence capabilities for document-level event argument extraction (2024.findings-acl)
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| Challenge: | Existing models for document-level event argument extraction (D-EAE) lack key feature forgetting and cross-event argument confusion. |
| Approach: | They propose a document-level event argument extraction method based on guided summarization and reasoning that leverages the emergence capabilities of large language models to highlight key event information. |
| Outcome: | The proposed method outperforms baseline models by 1.3% F1 and 1.6% F1 on WIKIEVENTS and RAMS. |
Prophecy Distillation for Boosting Abstractive Summarization (2024.lrec-main)
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| Challenge: | Abstractive summarization models with maximum likelihood estimation generate unfaithful facts alongside ambiguous focus. |
| Approach: | They propose a framework which learns a regular summarization model to mimic the behavior of being guided by prophecy for boosting abstractive summaries. |
| Outcome: | The proposed model achieves new or matched state-of-the-art on four well-known datasets. |