SUPERT: Towards New Frontiers in Unsupervised Evaluation Metrics for Multi-Document Summarization (2005.03724v1)

Abstract: We study unsupervised multi-document summarization evaluation metrics, which require neither human-written reference summaries nor human annotations (e.g. preferences, ratings, etc.). We propose SUPERT, which rates the quality of a summary by measuring its semantic similarity with a pseudo reference summary, i.e. selected salient sentences from the source documents, using contextualized embeddings and soft token alignment techniques. Compared to the state-of-the-art unsupervised evaluation metrics, SUPERT correlates better with human ratings by 18-39%. Furthermore, we use SUPERT as rewards to guide a neural-based reinforcement learning summarizer, yielding favorable performance compared to the state-of-the-art unsupervised summarizers. All source code is available at https://github.com/yg211/acl20-ref-free-eval.

An Expert Overview of "SUPERT: Towards New Frontiers in Unsupervised Evaluation Metrics for Multi-Document Summarization"

The authors introduce SUPERT, a novel unsupervised evaluation metric specifically designed for multi-document summarization, catering to the need for reducing human involvement in evaluating summary quality. This innovation stands out by eliminating the need for human-written reference summaries and annotations, instead opting for automatically generated pseudo references to gauge semantic similarity. The pseudo reference summarization is a significant shift from current paradigms, offering a computationally efficient alternative that maintains a high correlation with human evaluations.

Contributions and Methodology

SUPERT leverages the strengths of contextualized embeddings and token alignment techniques to evaluate summaries without human input. It abandons direct human annotations and instead focuses on measuring the relevance of summaries through semantic content overlap with pseudo references. By exploiting advanced text encoders like BERT and Sentence-BERT (SBERT), SUPERT is capable of capturing nuanced semantic information in text, which is pivotal for evaluating summary quality in a reference-free context.

The process involves two critical phases:

1. Salient Information Extraction: From the input source documents, important sentences are identified to assemble a pseudo reference summary. This is accomplished through various heuristic and graph-based strategies, including position-based extraction and affinity clustering.
2. Semantic Similarity Measurement: The summary-to-be-evaluated is compared to the pseudo reference using the aforementioned embeddings and alignment methodologies. In particular, SUPERT utilizes strategies such as minimizing word mover's distances to align tokens from different documents seamlessly.

Performance and Results

The results indicate that SUPERT exhibits an impressive correlation with human assessment scores, outperforming existing state-of-the-art unsupervised evaluation metrics by 18-39% in terms of Kendall's τ correlation. These findings are consistent across datasets from the Text Analysis Conference (TAC), showcasing SUPERT's effectiveness in various scenarios of multi-document summarization.

Moreover, when paired with a reinforcement learning framework, SUPERT further proves its utility. It is used as a reward function in training neural-based summarizers, yielding superior performance relative to competing unsupervised methods. The application of SUPERT in this context suggests promising potential in overcoming the limitations imposed by data scarcity in reinforcement-learning-based summarization models.

Implications and Future Directions

SUPERT's development marks a significant step toward refining automated text assessment frameworks. By enabling unsupervised evaluation, researchers have opened up pathways for scaling summarization tasks with reduced human intervention. The combination of sophisticated embeddings and evaluation strategies ensures that machine-generated summaries can be judged with enhanced precision and reliability.

From a practical standpoint, SUPERT could influence the design of future summarization systems and metrics that aim to be both efficient and closely aligned with human judgment. Theoretically, this approach challenges and extends current understanding of summary evaluation, emphasizing the importance of semantic richness over mere lexical matching.

The research community can view SUPERT as a benchmark for further innovation. Future work might involve exploring additional contextual embeddings, refining pseudo reference construction, and expanding SUPERT to diverse document types beyond news-based articles. As artificial intelligence evolves, systems like SUPERT could become instrumental in developing more robust, autonomous text evaluation frameworks. The scalability and reduced reliance on human oversight provided by SUPERT represent significant milestones in the field of computational linguistics and AI.