Papers by Frederic Kirstein
What’s under the hood: Investigating Automatic Metrics on Meeting Summarization (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing evaluation metrics do not capture meeting-specific errors, leading to ineffective assessment. |
| Approach: | They examine the relationship between established metrics and human evaluations to determine what challenges and errors are captured by correlating metric scores with human evaluation. |
| Outcome: | The proposed measures show weak correlations with human evaluations and a third of the correlations show error masking. |
You need to MIMIC to get FAME: Solving Meeting Transcript Scarcity with Multi-Agent Conversations (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing tools for meeting summarization are limited due to privacy and expensive manual annotation. |
| Approach: | They propose a meeting synthesis framework that generates meeting transcripts on a given knowledge source by defining psychologically grounded participant profiles, outlining the conversation, and orchestrating a large language model (LLM) debate. |
| Outcome: | The proposed framework generates meeting transcripts on a given knowledge source by defining psychologically grounded participant profiles, outlining the conversation, and orchestrating a large language model debate. |
How Large Language Models are Transforming Machine-Paraphrase Plagiarism (2022.emnlp-main)
Copied to clipboard
| Challenge: | Autoregressive paraphrasing tools can be used to generate convincing plagiarized texts with minimal effort. |
| Approach: | They evaluate the detection performance of large autoregressive models for machine-paraphrase generation on scientific articles from arXiv, student theses, and Wikipedia. |
| Outcome: | The proposed models generate paraphrases indistinguishable from original work and human experts rate the quality of generated examples as high as originals. |
Re-FRAME the Meeting Summarization SCOPE: Fact-Based Summarization and Personalization via Questions (2025.findings-emnlp)
Copied to clipboard
| Challenge: | FRAME reframes summarization as a semantic enrichment task . SCOPE is a reason-out-loud protocol that has the model build a reasoning trace . |
| Approach: | They propose a modular pipeline that reframes summarization as a semantic enrichment task. |
| Outcome: | The proposed pipeline reduces hallucinations and omissions by 2 out of 5 points . SCOPE improves knowledge fit and goal alignment over prompt-only baselines . |
Tell me what I need to know: Exploring LLM-based (Personalized) Abstractive Multi-Source Meeting Summarization (2024.emnlp-industry)
Copied to clipboard
| Challenge: | Existing methods for meeting summarization rely on transcripts and generate generic summaries, failing to contextualize long discussions and to tailor information to individual preferences and productivity requirements. |
| Approach: | They propose a multi-source approach that considers supplementary materials and generates a summary from this enriched transcript. |
| Outcome: | The proposed model increases summary relevance by 9% and produces more content-rich outputs. |