SummVis: Interactive Visual Analysis of Models, Data, and Evaluation for Text Summarization (2104.07605v2)

Abstract: Novel neural architectures, training strategies, and the availability of large-scale corpora haven been the driving force behind recent progress in abstractive text summarization. However, due to the black-box nature of neural models, uninformative evaluation metrics, and scarce tooling for model and data analysis, the true performance and failure modes of summarization models remain largely unknown. To address this limitation, we introduce SummVis, an open-source tool for visualizing abstractive summaries that enables fine-grained analysis of the models, data, and evaluation metrics associated with text summarization. Through its lexical and semantic visualizations, the tools offers an easy entry point for in-depth model prediction exploration across important dimensions such as factual consistency or abstractiveness. The tool together with several pre-computed model outputs is available at https://github.com/robustness-gym/summvis.

• The paper presents SummVis as a novel tool that integrates model, data, and evaluation analysis for text summarization.
• It uses interactive visualizations to compare source texts, reference summaries, and generated outputs, enhancing understanding of model performance and failure modes.
• The tool employs advanced lexical and semantic similarity measures and integrates with HuggingFace APIs to facilitate detailed analysis and debugging.

Interactive Visual Analysis of Models, Data, and Evaluation for Text Summarization

The paper introduces an open-source tool designed for the comprehensive visual analysis of text summarization systems, addressing a significant limitation in the interpretability of neural network models in this field. Despite substantial advancements in automatic text summarization, understanding the true capabilities and limitations of summarization models remains challenging due to their inherent black-box nature and the often inadequate traditional evaluation metrics. The proposed tool aims to bridge this gap by providing an interactive, fine-grained analytical framework that encompasses model evaluation, data examination, and metric assessment.

Key Features and Contributions

The tool—referred to in the paper but not given a specific name—offers a suite of visual analysis capabilities that enhance the interpretability of text summarization outputs. The interface supports nuanced comparisons among source documents, reference summaries, and generated summaries across three primary analytical modes:

1. Model Analysis: Facilitates the understanding of a model's ability to produce abstractions and retain factual consistency, by contrasting generated summaries with the source text.
2. Data Analysis: Allows for the evaluation of reference summaries' abstraction level and factual consistency by comparing them with original source documents.
3. Evaluation Analysis: Provides insights into the word- and phrase-level relationships that inform automatic evaluation metrics such as ROUGE and BERTScore, offering a more detailed perspective than aggregate scores alone.

These functionalities are underpinned by both lexical and semantic similarity measures, which align not only the surface form of the texts but also their underlying meanings using advanced word embedding techniques.

System Architecture and Usability

Implemented as a Streamlit application, the tool incorporates a customizable HTML/JavaScript component for enhanced user interaction. It is pre-loaded with predictions from state-of-the-art summarization models for benchmark datasets, facilitating immediate analysis. Moreover, it integrates with the HuggingFace Datasets API, allowing users to import custom data and models seamlessly.

Case Study and Practical Implications

The paper includes a comprehensive case paper focusing on the persistent issue of hallucinations in summarization models. By analyzing examples where neural models fabricate information, the authors demonstrate how the tool can pinpoint failure modes potentially originating from training data artifacts. Furthermore, the paper scrutinizes the alignment between hallucinated content and evaluation metrics, highlighting how tools like BERTScore may inadvertently inflate scores despite factual inaccuracies.

Conclusion and Future Directions

The tool exemplifies a significant step forward in the development of resources for model evaluation and debugging in the domain of text summarization. By combining model, data, and evaluation analysis into a cohesive visual framework, it enriches the understanding of current summarization methodologies. While the paper primarily showcases the tool's application in identifying model shortcomings, future research could extend its capabilities and apply similar methodologies to other NLG tasks to enhance robustness and mitigate biases.

This comprehensive approach to summarization analysis not only aids in more accurate model development but also lays the groundwork for better evaluation frameworks that may benefit the broader NLP community.