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Iterative peer-editing with audio improves human speech summaries

By PulseAugur Editorial · [1 sources] ·

Researchers explored ten different workflows for creating human-annotated speech summarization datasets, varying input modalities and editing processes. They found that summaries derived directly from audio were less informative than those from transcripts. However, implementing an iterative peer-editing process with audio inputs significantly improved summary quality, making them as informative as transcript-based summaries and even LLM-generated summaries. AI

IMPACT Introduces a novel method for creating high-quality speech summarization datasets, which could improve future LLM training and evaluation.

RANK_REASON Academic paper detailing a new methodology for dataset creation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Iterative peer-editing with audio improves human speech summaries

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Academic paper detailing a new methodology for dataset creation. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Najim Dehak ·

    Beyond Transcripts: Iterative Peer-Editing with Audio Unlocks High-Quality Human Summaries of Conversational Speech

    There are not enough established benchmarks for the task fo speech summarization. Creating new benchmarks demands human annotation, as LLMs could embed systemic errors and bias into datasets. We test ten annotation workflows varying input modality (audio, transcript, or both) and…

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