MS2: Multi-Document Summarization of Medical Studies (2104.06486v3)

Abstract: To assess the effectiveness of any medical intervention, researchers must conduct a time-intensive and highly manual literature review. NLP systems can help to automate or assist in parts of this expensive process. In support of this goal, we release MS^2 (Multi-Document Summarization of Medical Studies), a dataset of over 470k documents and 20k summaries derived from the scientific literature. This dataset facilitates the development of systems that can assess and aggregate contradictory evidence across multiple studies, and is the first large-scale, publicly available multi-document summarization dataset in the biomedical domain. We experiment with a summarization system based on BART, with promising early results. We formulate our summarization inputs and targets in both free text and structured forms and modify a recently proposed metric to assess the quality of our system's generated summaries. Data and models are available at https://github.com/allenai/ms2

• The paper presents a dataset that streamlines multi-document summarization by leveraging systematic literature reviews in the biomedical domain.
• It employs structured representations, including Population, Intervention, Outcome elements, to facilitate the aggregation of contradictory study findings.
• Novel evaluation metrics such as the ΔEI distance and F1 scores demonstrate the system’s ability to assess summary coherence and factual accuracy.

Overview of "MS: A Dataset for Multi-Document Summarization of Medical Studies"

The paper entitled "MS: A Dataset for Multi-Document Summarization of Medical Studies" explores the challenge of synthesizing biomedical literature, a task critical for evaluating medical interventions but inherently time-consuming and labor-intensive. The authors introduce the MS dataset, comprising over 470,000 documents and 20,000 summaries sourced from scientific literature. This dataset is a significant resource in the domain of multi-document summarization (MDS), focusing specifically on the biomedical domain where contradictory evidence and complex data synthesis are prevalent.

Dataset Construction and Characteristics

The MS dataset is constructed by extracting documents and summaries from systematic literature reviews. Systematic reviews consolidate findings from numerous studies to yield high-quality evidence suitable for medical and public health decisions. The dataset stands out by providing a structured approach to MDS grounded in real-world applications. Its components, including backgrounds, summaries, and structured representations such as Population, Intervention, Outcome (PIO) elements, facilitate a nuanced understanding of aggregated evidence.

Given the inherent challenge of summarizing contradictory information across studies, the dataset incorporates tools for evaluating summary quality. The authors leverage systems like BART for summarization purposes, offering initial results that hint at the potential for automated literature review synthesis.

Key Technical Insights

• Multi-Document Summarization in Biomedical Domain: The work expands on existing MDS capabilities by targeting specialized scientific text. This includes handling medical terms, paper types, and extracting PICO elements that are integral to systematic reviews.
• Data Representation: The dataset not only provides text form but also structured data representation. This dual format supports tasks like aggregation of paper results and quality assessment of overall summaries, addressing the multifaceted nature of biomedical evidence synthesis.
• Evaluation Metrics: Novel evaluation metrics such as the $\Delta$EI distance metric and F1 scores for direction consistency provide quantitative measures for assessing the coherence and factual accuracy of generated summaries.

Implications and Future Directions

This research has practical implications, especially in the medical field where timely, accurate literature reviews influence decision-making and policy. The MS dataset and accompanying models could streamline the synthesis of biomedical research, allowing for quicker updates as new studies are published. Theoretically, this work tackles the complexity of modeling contradictions, a prevalent issue in scientific literature, and opens avenues for developing more robust NLP systems that can handle such nuances effectively.

Future developments may focus on improving the structured representations used, enhancing metric accuracy, and exploring joint retrieval-summarization models to better address the challenges of expanding publication volumes. Continued advancements in this area can significantly enhance automated systems' capabilities in synthesizing expansive, diverse scientific literature, contributing to faster and more reliable medical research outcomes.