Papers-to-Posts: Supporting Detailed Long-Document Summarization with an Interactive LLM-Powered Source Outline
Marissa Radensky, Daniel S. Weld, Joseph Chee Chang, Pao Siangliulue,, Jonathan Bragg

TL;DR
This paper introduces an interactive outline-based system called Papers-to-Posts that enhances long-document summarization by allowing users to control content coverage, improving satisfaction and editing efficiency in blog post creation.
Contribution
It presents a novel interactive mechanism for controllable summarization using source outlines, implemented in a system for research-blog post authoring, with demonstrated user benefits.
Findings
Increased user satisfaction with content coverage.
Higher editing efficiency and control.
Participants found it easier to include key insights.
Abstract
Compressing long and technical documents (e.g., >10 pages) into shorter-form articles (e.g., <2 pages) is critical for communicating information to different audiences, for example, blog posts of scientific research paper or legal briefs of dense court proceedings. While large language models (LLMs) are powerful tools for condensing large amounts of text, current interfaces to these models lack support for understanding and controlling what content is included in a detailed summarizing article. Such capability is especially important for detail- and technical-oriented domains, in which tactical selection and coherent synthesis of key details is critical for effective communication to the target audience. For this, we present interactive reverse source outlines, a novel mechanism for controllable long-form summarization featuring outline bullet points with automatic point selections that…
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Taxonomy
TopicsArtificial Intelligence in Law
