CLIFF: Contrastive Learning for Improving Faithfulness and Factuality in Abstractive Summarization (2109.09209v1)

Abstract: We study generating abstractive summaries that are faithful and factually consistent with the given articles. A novel contrastive learning formulation is presented, which leverages both reference summaries, as positive training data, and automatically generated erroneous summaries, as negative training data, to train summarization systems that are better at distinguishing between them. We further design four types of strategies for creating negative samples, to resemble errors made commonly by two state-of-the-art models, BART and PEGASUS, found in our new human annotations of summary errors. Experiments on XSum and CNN/Daily Mail show that our contrastive learning framework is robust across datasets and models. It consistently produces more factual summaries than strong comparisons with post error correction, entailment-based reranking, and unlikelihood training, according to QA-based factuality evaluation. Human judges echo the observation and find that our model summaries correct more errors.

Analysis of the CLIFF Framework for Enhancing Faithfulness in Abstractive Summarization

The research paper titled "CLIFF: Contrastive Learning for Improving Faithfulness and Factuality in Abstractive Summarization" addresses a critical challenge faced by modern summarization models: the tendency to generate outputs that are factually inaccurate or unfaithful to the source text. The authors propose a novel framework, CLIFF, which applies contrastive learning to enhance the ability of summarization models to produce more factually consistent summaries. This paper offers a comprehensive examination of the CLIFF approach, detailing its implementation, evaluation, and implications for the field of natural language processing.

Overview of the CLIFF Methodology

The CLIFF framework employs a contrastive learning strategy, which is pivotal in training summarization models to distinguish between factual and erroneous summaries. The main idea is to create both positive samples (factual summaries) and negative samples (erroneous summaries), training the model to differentiate between these. Four strategies for generating negative samples are explored:

1. Entity Swap (SwapEnt): This involves swapping named entities within the reference summaries with entities of the same type from the source document, encouraging the model to recognize errors commonly arising from incorrect entity replacements.
2. Mask-and-Fill Strategy (MaskEnt and MaskRel): Utilizing BART, a pre-trained Transformer model, this approach masks entities and relations in the reference summaries and fills them with potentially erroneous alternatives, simulating real-world errors in generated summaries.
3. Source-Conditioned Regeneration (RegenEnt and RegenRel): This technique uses truncated reference summaries as prompts for a summarizer conditioned on the source document to generate new summaries, incorporating potential errors grounded in the document context.
4. System Generation from Low Confidence (SysLowCon): Summaries are generated by the model with a focus on low confidence outputs on proper nouns and numbers, which are indicators of potential errors.

Experimental Framework and Evaluation

The authors conducted extensive experiments on two well-known datasets, XSum and CNN/DailyMail, demonstrating that CLIFF offers consistent improvements over other methods. The evaluation includes both automatic metrics and human judgment, with a primary focus on QuestEval—a QA-based metric that reflects human judgment on factual consistency. In comparison with methods like entailment-based reranking and unlikelihood training, the CLIFF framework consistently generates more informative and factually accurate summaries.

The CLIFF models are fine-tuned from BART and PEGASUS, showcasing the robustness of the method across different architectures. The contrastive learning objective is shown to outperform traditional cross-entropy training by incorporating a new loss component that emphasizes the discernment of factual integrity.

Implications and Future Directions

The CLIFF framework's advancements open up potential for further research in improving factuality in generative tasks. Its focus on integrating negative samples drawn from realistic assembly processes allows the training models to better mimic and counteract common errors, thus bridging the gap between model-generated summaries and human-authored ones. Future work could explore extending this method to other text generation domains, such as dialogue systems, where maintaining factual correctness is equally crucial.

Moreover, the success of various negative sample generation strategies indicates that combining them may further enhance the model's performance. This could inspire development in multi-strategy sample generation frameworks, potentially incorporating adversarial approaches to introduce even more varied and nuanced errors.

Conclusion

The CLIFF framework represents a significant methodological contribution to enhancing the factuality and faithfulness of abstractive summarization models. By leveraging contrastive learning, it addresses a critical limitation of current summarization systems and points towards new possibilities for improving text generation fidelity. As the search for more reliable and accurate AI systems continues, CLIFF provides a promising foundation for ongoing research and development in the field.