SimCLS: A Simple Framework for Contrastive Learning of Abstractive Summarization (2106.01890v1)

Abstract: In this paper, we present a conceptually simple while empirically powerful framework for abstractive summarization, SimCLS, which can bridge the gap between the learning objective and evaluation metrics resulting from the currently dominated sequence-to-sequence learning framework by formulating text generation as a reference-free evaluation problem (i.e., quality estimation) assisted by contrastive learning. Experimental results show that, with minor modification over existing top-scoring systems, SimCLS can improve the performance of existing top-performing models by a large margin. Particularly, 2.51 absolute improvement against BART and 2.50 over PEGASUS w.r.t ROUGE-1 on the CNN/DailyMail dataset, driving the state-of-the-art performance to a new level. We have open-sourced our codes and results: https://github.com/yixinL7/SimCLS. Results of our proposed models have been deployed into ExplainaBoard platform, which allows researchers to understand our systems in a more fine-grained way.

An Analytical Overview of SimCLS: A Contrastive Learning Framework for Abstractive Summarization

The paper under discussion introduces SimCLS, a framework designed to enhance the quality of abstractive summarization. SimCLS addresses a well-known challenge inherent in sequence-to-sequence (Seq2Seq) neural models: the discrepancy between training objectives based on Maximum Likelihood Estimation (MLE) and evaluation metrics such as ROUGE. This discrepancy, often referred to as exposure bias, results in models that may be effective during training but falter during the inference phase.

Methodology

SimCLS is characterized by a generate-then-evaluate approach that uses contrastive learning. The process begins with the generation of candidate summaries using a Seq2Seq model trained with MLE. Subsequently, a separate evaluation model is tasked with ranking these summaries. The novelty lies in the evaluation model being trained via contrastive learning, leveraging the diverse output from the Seq2Seq model to learn effective discriminative features without direct reference to gold-standard summaries.

1. Candidate Generation: Utilizing well-established architectures such as BART and PEGASUS, the framework generates various candidate summaries through different sampling strategies.
2. Reference-Free Evaluation: A model based on RoBERTa predicts the quality of each candidate. The framework's key innovation lies in employing a ranking loss in contrastive learning to enhance reference-free evaluations, thus enabling the selection of the best candidate.
3. Contrastive Training: The evaluation model is trained using a ranking loss that penalizes lower-quality candidates more harshly, thus fine-tuning the discrimination capabilities of the evaluation framework.

Experimental Results

SimCLS was evaluated on datasets like CNN/DailyMail and XSum. Notable results include a 2.51 improvement in ROUGE-1 scores over BART and 2.50 over PEGASUS on the CNN/DailyMail dataset. Such improvements underscore the framework's ability to bridge the gap between training and evaluation objectives effectively. The paper further establishes that the gains achieved are not merely statistical artifacts but rather indicative of genuinely improved summarization quality, validated by semantic similarity metrics such as BERTScore.

Implications and Future Directions

Practically, SimCLS's framework presents a robust methodology for obtaining high-quality abstractive summaries that align closely with human judgment. Theoretically, this research lays the groundwork for further exploration into the advantages of decoupling generation and evaluation training phases. Future research directions might include extending this approach to other natural language generation tasks or exploring the integration of SimCLS with transformer-based architectures to push the boundaries of summary generation quality further.

In conclusion, the contributions of this paper extend beyond traditional reinforcement learning methods to address critical disparities in sequence modeling, offering a fresh avenue for both incremental advancements and paradigmatic shifts in abstractive summarization.