{"version":1,"type":"story","url":"https://digestai.news/story/researchers-fine-tune-406m-model-for-meeting-summaries-with-retrieved","json":"https://digestai.news/story/researchers-fine-tune-406m-model-for-meeting-summaries-with-retrieved.json","markdown":"https://digestai.news/story/researchers-fine-tune-406m-model-for-meeting-summaries-with-retrieved.md","slug":"researchers-fine-tune-406m-model-for-meeting-summaries-with-retrieved","headline":"Researchers fine-tune 406M model for meeting summaries with retrieved text spans","summary":"A new paper on arXiv introduces a method called *Retrieved-Span Training* to improve query-focused meeting summarization. The approach fine-tunes a 406M-parameter Fusion-in-Decoder model on retrieved text spans of up to 2,000 words instead of full transcripts. When tested on the QMSum dataset, the model achieves a **36.33 ROUGE-1** score, compared to **35.41** for a 1.2B-parameter baseline. The authors note the difference is not statistically significant, but the smaller model uses **one-third fewer parameters** and **less than half the peak inference memory** of the larger one.\n\nThe paper also shows that replacing the first 4,500 words of a transcript with 2,000 retrieved words improves performance by **1.55 ROUGE-1** on test data. Under a single prompt and scorer, the 406M model outperforms five proprietary hosted models by at least **6.2 ROUGE-1**, though the results lack human or factuality evaluation. The findings are limited to QMSum and automated metrics.","keyPoints":["406M-parameter model fine-tuned on 2,000-word retrieved text spans scores 36.33 ROUGE-1 on QMSum","Smaller model uses one-third fewer parameters and half the memory of a 1.2B baseline","Replacing first 4,500 transcript words with 2,000 retrieved words boosts performance by 1.55 ROUGE-1"],"whyItMatters":"Efficient summarization of long meetings could reduce costs for transcription and retrieval systems, while smaller models lower deployment barriers for edge devices or low-resource settings.","category":{"slug":"research","name":"Research","url":"https://digestai.news/category/research"},"entities":{"companies":[],"models":["Fusion-in-Decoder","QMSum"],"people":[]},"firstPublishedAt":"2026-09-23T04:00:00Z","updatedAt":"2026-09-23T04:00:00Z","sourceCount":1,"hasPrimarySource":true,"sources":[{"outlet":"arXiv cs.CL","title":"Retrieved-Span Training for Efficient Query-Focused Meeting Summarization on QMSum","url":"https://arxiv.org/abs/2609.25028","publishedAt":"2026-09-23T04:00:00Z","type":"primary","primary":true,"lead":true}],"sourceNotes":null,"discussions":[],"thread":null,"cite":{"text":"Digest AI, \"Researchers fine-tune 406M model for meeting summaries with retrieved text spans\", 23 September 2026, https://digestai.news/story/researchers-fine-tune-406m-model-for-meeting-summaries-with-retrieved","publisher":"Digest AI","title":"Researchers fine-tune 406M model for meeting summaries with retrieved text spans","datePublished":"2026-09-23T04:00:00Z","url":"https://digestai.news/story/researchers-fine-tune-406m-model-for-meeting-summaries-with-retrieved"},"generatedBy":"Written by Digest AI's editorial model from the linked sources; the sources are the record.","license":"Headlines, digests and key points are written by Digest AI and may be quoted with a link to the story page. Linked articles belong to their publishers. Terms: https://digestai.news/terms#reuse"}