Neural Text Summarization: A Critical Evaluation (1908.08960v1)

Abstract: Text summarization aims at compressing long documents into a shorter form that conveys the most important parts of the original document. Despite increased interest in the community and notable research effort, progress on benchmark datasets has stagnated. We critically evaluate key ingredients of the current research setup: datasets, evaluation metrics, and models, and highlight three primary shortcomings: 1) automatically collected datasets leave the task underconstrained and may contain noise detrimental to training and evaluation, 2) current evaluation protocol is weakly correlated with human judgment and does not account for important characteristics such as factual correctness, 3) models overfit to layout biases of current datasets and offer limited diversity in their outputs.

Critical Evaluation of Neural Text Summarization

The paper "Neural Text Summarization: A Critical Evaluation" presents an in-depth examination of the prevailing approaches and methodologies in the domain of neural text summarization. The authors critically assess the current state of the art, identifying key areas that hinder substantive progress in the field. The paper focuses on three primary dimensions: datasets, evaluation metrics, and model behavior, each highlighted as having significant limitations.

Key Findings and Contributions

1. Dataset Limitations: The paper highlights the inadequacy of existing datasets used for training and evaluating summarization models. A significant concern is the automatic collection process that often results in uncurated datasets containing noise, thus making the training process less effective. The authors argue that such datasets are underconstrained, leading to varied interpretations of what constitutes an 'important' fragment of text. This variance is quantified through human studies that demonstrate substantial disagreement among annotators regarding the importance of text elements when left unconstrained.
2. Evaluation Protocol Shortcomings: Another critical point raised is the weak correlation between current automatic evaluation metrics, particularly ROUGE, and human judgment. The paper presents compelling evidence that current evaluation protocols do not adequately capture dimensions such as relevance, consistency, fluency, and coherence. This disconnect is exacerbated by the fact that ROUGE metrics largely depend on lexical overlaps, thereby neglecting semantic correctness and factual consistency. Their analysis indicates that ROUGE scores align poorly with human evaluations, especially within the context of abstractive summarization models.
3. Model Over-reliance on Narrative Structures: Models trained on news data exhibit a pronounced bias towards learning structural patterns prevalent in journalism, such as the 'Inverted Pyramid' style. This style results in the most critical information being concentrated at the beginning of the article, skewing model predictions towards these initial sections. The authors emphasize that while such heuristics can inflate performance metrics on news-based datasets, they fail to generalize to other text forms lacking these layout biases, such as legal or scientific documents.
4. Diversity and Overfitting Concerns: An analysis of various state-of-the-art models reveals limited diversity in generated summaries, suggesting a propensity towards overfitting to dataset-specific structures. The paper demonstrates that despite apparent architectural and methodological differences, outputs among different models show high overlap, calling into question the novel contributions of recent approaches.

Implications and Future Directions

The insights provided by this paper underscore the need for the text summarization community to develop more rigorous and aligned datasets, evaluation metrics, and modeling strategies. Ensuring datasets are well-constrained and tailored to specific summarization tasks would mitigate some identified deficiencies. Moreover, advancement in evaluation protocols that encapsulate a broader spectrum of summarization qualities, such as factual accuracy and semantic consistency, is necessary.

Furthermore, the exploration of modeling approaches decoupled from specific domain biases or that incorporate mechanisms to adapt models to diverse narrative structures could foster more generalized solutions. Future research could focus on disentangling the dependence on specific structures inherent to datasets and investigate methods for adaptive feature learning that support a wider range of document types.

In conclusion, this critical evaluation provides a foundation for rethinking the methodological frameworks in neural text summarization and offers a pathway towards achieving more robust, universally applicable summarization techniques.