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In-depth Research Impact Summarization through Fine-Grained Temporal Citation Analysis

Hiba Arnaout, Noy Sternlicht, Tom Hope, Iryna Gurevych

TL;DR

The paper tackles the inadequacy of raw citation counts for assessing scientific impact by proposing time-aware impact summaries derived from impact-revealing citation contexts. It introduces a two-stage, LLM-based framework: first extracting fine-grained intents behind citations, then generating structured, time-indexed impact narratives. A new dataset extends PST-Bench to 4k contexts, and an automated, reference-free evaluation framework assesses faithfulness, coverage, and informativeness, with human judges showing moderate-to-strong correlation. Expert feedback confirms practical value, and the authors release code and data to support future research on nuanced, time-evolving scholarly impact.

Abstract

Understanding the impact of scientific publications is crucial for identifying breakthroughs and guiding future research. Traditional metrics based on citation counts often miss the nuanced ways a paper contributes to its field. In this work, we propose a new task: generating nuanced, expressive, and time-aware impact summaries that capture both praise (confirmation citations) and critique (correction citations) through the evolution of fine-grained citation intents. We introduce an evaluation framework tailored to this task, showing moderate to strong human correlation on subjective metrics such as insightfulness. Expert feedback from professors reveals a strong interest in these summaries and suggests future improvements.

In-depth Research Impact Summarization through Fine-Grained Temporal Citation Analysis

TL;DR

The paper tackles the inadequacy of raw citation counts for assessing scientific impact by proposing time-aware impact summaries derived from impact-revealing citation contexts. It introduces a two-stage, LLM-based framework: first extracting fine-grained intents behind citations, then generating structured, time-indexed impact narratives. A new dataset extends PST-Bench to 4k contexts, and an automated, reference-free evaluation framework assesses faithfulness, coverage, and informativeness, with human judges showing moderate-to-strong correlation. Expert feedback confirms practical value, and the authors release code and data to support future research on nuanced, time-evolving scholarly impact.

Abstract

Understanding the impact of scientific publications is crucial for identifying breakthroughs and guiding future research. Traditional metrics based on citation counts often miss the nuanced ways a paper contributes to its field. In this work, we propose a new task: generating nuanced, expressive, and time-aware impact summaries that capture both praise (confirmation citations) and critique (correction citations) through the evolution of fine-grained citation intents. We introduce an evaluation framework tailored to this task, showing moderate to strong human correlation on subjective metrics such as insightfulness. Expert feedback from professors reveals a strong interest in these summaries and suggests future improvements.

Paper Structure

This paper contains 39 sections, 20 figures, 18 tables.

Table of Contents

  1. Introduction
  2. Method
  3. Task definition
  4. Identifying impact-revealing citation intents
  5. Generating scientific impact summaries
  6. Evaluation metrics
  7. A new dataset for identifying impact-revealing citation contexts
  8. Experiments
  9. In-context-learning for fine-grained intent generation
  10. Can existing intent classifiers detect impact-revealing citations?
  11. Ablation study for impact summary generation
  12. Practical applications
  13. Human Evaluation
  14. Related Work
  15. Conclusion
  16. ...and 24 more sections

Figures (20)

  • Figure 1: We propose a new task to summarize a paper’s evolving impact over time, by analyzing impact-revealing citation contexts, reflecting both praise (confirming ideas) and critique (calls for correction). Our summary of koller1997hierarchically reveals its impact trajectory—adaptation, critique, and rediscovery—offering deeper insight than its $\sim$1.4k cite count.
  • Figure 2: Effect of different number of shots (K) on detecting impact-revealing citations. Zero-shot: horizontal dashed lines; always predict impact-revealing baseline: dotted lines. This shows that adding a reasonable number of examples (around 50) is sufficient to yield a 60% improvement in recall.
  • Figure 3: Pairwise comparison. Relevance: Which summary better reflects the paper's actual impact? Insightfulness: Which summary offers more valuable or novel information about the paper's usage? Professors consider our summaries more relevant and insightful.
  • Figure 4: Prompt for generating and classifying fine-grained intents.
  • Figure 5: Prompt for generating an impact summary about a research paper.
  • ...and 15 more figures

Theorems & Definitions (4)

  • Definition 1
  • Definition 2
  • Definition 3
  • Definition 4