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On the Evaluation of Machine-Generated Reports

James Mayfield, Eugene Yang, Dawn Lawrie, Sean MacAvaney, Paul McNamee, Douglas W. Oard, Luca Soldaini, Ian Soboroff, Orion Weller, Efsun Kayi, Kate Sanders, Marc Mason, Noah Hibbler

TL;DR

This paper defines ARGUE, an evaluation framework for automated long-form report generation that emphasizes responsiveness to a user-specified information need, grounding in a fixed document collection, verifiability via citations, and completeness. It integrates ideas from IR, summarization, QA, and RAG to propose a nugget-based evaluation where information needs are articulated as questions with answer sets attested in documents. The framework outlines three phases—data creation, input distribution, and scoring—along with roles (Report Requester, Audience, Writer, Assessor) and mechanisms for citation validity and recall over nuggets. By focusing on reusability and human-ground-truth grounding, ARGUE aims to drive progress in building systems capable of producing complete, accurate, and verifiable long-form reports relevant to complex information needs, while acknowledging current limitations and outlining practical evaluation guidance.

Abstract

Large Language Models (LLMs) have enabled new ways to satisfy information needs. Although great strides have been made in applying them to settings like document ranking and short-form text generation, they still struggle to compose complete, accurate, and verifiable long-form reports. Reports with these qualities are necessary to satisfy the complex, nuanced, or multi-faceted information needs of users. In this perspective paper, we draw together opinions from industry and academia, and from a variety of related research areas, to present our vision for automatic report generation, and -- critically -- a flexible framework by which such reports can be evaluated. In contrast with other summarization tasks, automatic report generation starts with a detailed description of an information need, stating the necessary background, requirements, and scope of the report. Further, the generated reports should be complete, accurate, and verifiable. These qualities, which are desirable -- if not required -- in many analytic report-writing settings, require rethinking how to build and evaluate systems that exhibit these qualities. To foster new efforts in building these systems, we present an evaluation framework that draws on ideas found in various evaluations. To test completeness and accuracy, the framework uses nuggets of information, expressed as questions and answers, that need to be part of any high-quality generated report. Additionally, evaluation of citations that map claims made in the report to their source documents ensures verifiability.

On the Evaluation of Machine-Generated Reports

TL;DR

This paper defines ARGUE, an evaluation framework for automated long-form report generation that emphasizes responsiveness to a user-specified information need, grounding in a fixed document collection, verifiability via citations, and completeness. It integrates ideas from IR, summarization, QA, and RAG to propose a nugget-based evaluation where information needs are articulated as questions with answer sets attested in documents. The framework outlines three phases—data creation, input distribution, and scoring—along with roles (Report Requester, Audience, Writer, Assessor) and mechanisms for citation validity and recall over nuggets. By focusing on reusability and human-ground-truth grounding, ARGUE aims to drive progress in building systems capable of producing complete, accurate, and verifiable long-form reports relevant to complex information needs, while acknowledging current limitations and outlining practical evaluation guidance.

Abstract

Large Language Models (LLMs) have enabled new ways to satisfy information needs. Although great strides have been made in applying them to settings like document ranking and short-form text generation, they still struggle to compose complete, accurate, and verifiable long-form reports. Reports with these qualities are necessary to satisfy the complex, nuanced, or multi-faceted information needs of users. In this perspective paper, we draw together opinions from industry and academia, and from a variety of related research areas, to present our vision for automatic report generation, and -- critically -- a flexible framework by which such reports can be evaluated. In contrast with other summarization tasks, automatic report generation starts with a detailed description of an information need, stating the necessary background, requirements, and scope of the report. Further, the generated reports should be complete, accurate, and verifiable. These qualities, which are desirable -- if not required -- in many analytic report-writing settings, require rethinking how to build and evaluate systems that exhibit these qualities. To foster new efforts in building these systems, we present an evaluation framework that draws on ideas found in various evaluations. To test completeness and accuracy, the framework uses nuggets of information, expressed as questions and answers, that need to be part of any high-quality generated report. Additionally, evaluation of citations that map claims made in the report to their source documents ensures verifiability.
Paper Structure (29 sections, 4 figures)

This paper contains 29 sections, 4 figures.

Table of Contents

  1. Introduction
  2. Requirements
  3. Report Requester:
  4. Report Audience:
  5. Report Writer:
  6. Report Request:
  7. Assessor:
  8. Background
  9. Report Writing
  10. Summarization
  11. Retrieval-Augmented Generation
  12. Evaluation
  13. Information Retrieval
  14. Summarization
  15. Question Answering
  16. ...and 14 more sections

Figures (4)

  • Figure 1: A pair of example Summary Content Units. Four semantically similar sentences from four different model summaries are grouped into two SCUs highlighting the key facts from those sentences. From pyramid.
  • Figure 2: Report sentence scoring. Answers to eight yes/no questions dictate an outcome for each input sentence. + indicates that the sentence is rewarded, - that it is penalized, and 0 that it does not affect the overall report score.
  • Figure 3: Example evaluation material for a report request.
  • Figure 4: Example report evaluation result.