TLDR: Extreme Summarization of Scientific Documents (2004.15011v3)

Abstract: We introduce TLDR generation, a new form of extreme summarization, for scientific papers. TLDR generation involves high source compression and requires expert background knowledge and understanding of complex domain-specific language. To facilitate study on this task, we introduce SciTLDR, a new multi-target dataset of 5.4K TLDRs over 3.2K papers. SciTLDR contains both author-written and expert-derived TLDRs, where the latter are collected using a novel annotation protocol that produces high-quality summaries while minimizing annotation burden. We propose CATTS, a simple yet effective learning strategy for generating TLDRs that exploits titles as an auxiliary training signal. CATTS improves upon strong baselines under both automated metrics and human evaluations. Data and code are publicly available at https://github.com/allenai/scitldr.

• The paper introduces Catts, a multitask learning method that jointly produces titles and TLDRs, significantly enhancing summary quality over baseline models.
• The authors develop SciTLDR, a dataset of over 5,400 TLDRs from scientific papers, enabling detailed exploration of extreme summarization techniques.
• The study demonstrates that using auxiliary title generation improves factual accuracy and informativeness in condensed scientific document summaries.

Extreme Summarization of Scientific Documents: An Analysis of TLDR Generation

Introduction

The paper "Tldr: Extreme Summarization of Scientific Documents" addresses the pressing need for efficient summarization methods by introducing a novel form known as TLDR generation. This technique emphasizes the creation of single-sentence summaries for scientific papers, facilitating quick comprehension and navigation of the exponentially growing body of scientific literature. The researchers present a comprehensive dataset and propose a learning strategy, Catts, which optimizes title generation as an auxiliary task to improve summary creation.

Methodology

The paper introduces SciTLDR, a multi-target dataset comprising over 5,411 TLDRs across 3,229 scientific documents. These summaries originate from two sources: author-written summaries on the OpenReview platform and those derived from peer reviews using an innovative annotation protocol. The protocol ensures high-quality summaries while minimizing the burden on annotators. The dataset enables a deeper exploration of summarization techniques in the academic domain.

The researchers propose Catts, a learning method leveraging Transformer-based models like BART. Catts employs multitask learning by generating both titles and TLDRs, using control codes to guide the summarization process. The approach capitalizes on the naturally occurring correlation between paper titles and content to address data scarcity challenges in scientific domains.

Results

The paper demonstrates substantial improvements in summary generation using Catts over baseline models. Specifically, Catts improves upon BART models by capitalizing on both abstract-only and AIC (abstract, introduction, conclusion) input spaces. In human evaluations, Catts-generated TLDRs were more informative and maintained factual accuracy compared to baseline outputs.

The dataset's design also allows the examination of various informational aspects within summaries, termed "nuggets." The nuanced analysis provides insights into how different types of information are emphasized, revealing that author-generated TLDRs often differ significantly from those derived from reviews.

Implications

This work holds significant implications for the field of natural language processing and AI-driven document processing. The ability to generate concise, accurate summaries rapidly can facilitate better information access, especially in academia. This might be further enhanced by integrating such tools into publication platforms or research libraries, augmenting scholarly communication.

Theoretically, this research highlights potential avenues for improving extreme summarization techniques by incorporating multitask learning frameworks and exploring the utility of auxiliary data signals like titles. Such advances could inform developments in other domains requiring sophisticated document summary capabilities.

Future Directions

Potential future work includes expanding the scope of TLDR generation to other academic disciplines, each with unique terminological and organizational characteristics. There is also a compelling opportunity to explore domain adaptation techniques, enabling models trained on one set of scientific articles to generalize effectively to others.

Furthermore, investigating the integration of citation contexts and deeper discourse roles could enrich summarization models, potentially leading to more informative and contextually nuanced summaries. Additionally, further exploration of evaluation metrics tailored specifically to extreme summarization tasks could refine assessments of model performance.

Conclusion

The paper provides a rigorous examination of TLDR generation for scientific documents, introducing a resourceful dataset and innovative algorithmic contributions. By addressing both practical and theoretical challenges in the domain of document summarization, the research sets a foundation for continued advancements in how AI processes and conveys complex scientific information effectively.