Challenge: Existing methods for extractive and abstractive summarization use token-level or sentence-level training objectives.
Approach: They propose a Contrastive Learning based re-ranking framework for one-stage summarization called CoLo.
Outcome: The proposed framework boosts extractive and abstractive results on CNN/DailyMail benchmarks while maintaining inference efficiency.

Similar Papers

SimCLS: A Simple Framework for Contrastive Learning of Abstractive Summarization (2021.acl-short)

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Challenge: Experimental results show that SimCLS can improve existing top-performing models by a large margin.
Approach: They propose a framework for abstractive summarization that is conceptually simple and empirically powerful.
Outcome: The proposed framework improves the performance of top-performing models by a large margin against existing top-scoring systems.
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.
Summary Level Training of Sentence Rewriting for Abstractive Summarization (D19-54)

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Challenge: Existing models rely on sentence-level rewards or suboptimal labels to achieve summary-level ROUGE scores.
Approach: They propose a model that extracts salient sentences from a document and paraphrases them to generate a summary.
Outcome: The proposed model improves on CNN/Daily Mail and New York Times datasets.
Ranking Sentences for Extractive Summarization with Reinforcement Learning (N18-1)

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Challenge: Abstractive summarization involves various text rewriting operations and has been identified as a sequence-to-sequence problem.
Approach: They propose a novel algorithm which globally optimizes the ROUGE evaluation metric through a reinforcement learning objective.
Outcome: The proposed algorithm outperforms state-of-the-art extractive and abstractive systems when evaluated automatically and by humans.
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.
GECSum: Generative Evaluation-Driven Sequence Level Contrastive Learning for Abstractive Summarization (2024.lrec-main)

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Challenge: Abstractive summarization is a technique in natural language processing that involves generating a summary of a source document by creating new sentences and phrases.
Approach: They propose a sequence-level contrastive learning framework that leverages the semantic understanding capabilities of the abstractive model itself to evaluate summary in reference-based settings.
Outcome: The proposed framework outperforms the state-of-the-art in four summarization datasets.
ConRAS: Contrastive In-context Learning Framework for Retrieval-Augmented Summarization (2026.findings-eacl)

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Challenge: Despite progress in natural language processing, the potential of contrastive learning remains unexplored.
Approach: They propose a framework that injects contrastive objectives into in-context learning-based retrieval-augmented summarization.
Outcome: The proposed framework outperforms state-of-the-art retrieval-augmented methods on three summarization benchmarks showing that it can distinguish between positive and negative samples without parameter updates.
CLIFF: Contrastive Learning for Improving Faithfulness and Factuality in Abstractive Summarization (2021.emnlp-main)

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Challenge: Existing methods for generating abstractive summarization are inconsistent and rely on heuristically created data for error handling.
Approach: They propose a contrastive learning formulation that leverages both positive and negative summaries to train summarization systems that are better at distinguishing between them.
Outcome: The proposed learning framework produces more factual summaries than strong comparisons with post error correction, entailment-based reranking, and unlikelihood training.
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.
Facet-Aware Evaluation for Extractive Summarization (2020.acl-main)

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Challenge: lexical overlap is a common evaluation metric for extractive summarization, but recent studies reveal its limitations.
Approach: They propose a facet-aware evaluation setup for better assessment of information coverage in extractive summaries.
Outcome: The proposed evaluation setup improves human correlation with extractive summarization datasets and improves comparative analysis.

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