Listen, Reason, and Segment: Aligning LALMs with Editorial Judgment for Media Chapterization
Large Audio Language Models (LALMs) have made rapid progress on standardized benchmarks, yet their deployment in practical media workflows, curation, archival indexing, and content distribution remains largely unrealized. We identify automated audio chapterization, the task of segmenting continuous audio streams into thematically coherent chapters, as a demanding and commercially consequential setting that exposes this gap. Chapterization is challenging because boundaries are defined less by objective acoustic events than by subjective editorial judgment, requiring models to reason sequentiall
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- LinkedLinked via arxiv author · 85%Tony Alex →
“Listen, Reason, and Segment: Aligning LALMs with Editorial Judgment for Media Chapterization”
- LinkedLinked via arxiv author · 85%Wish Suharitdamrong →
“Listen, Reason, and Segment: Aligning LALMs with Editorial Judgment for Media Chapterization”
- LinkedLinked via arxiv author · 85%Sara Atito →
“Listen, Reason, and Segment: Aligning LALMs with Editorial Judgment for Media Chapterization”
- LinkedLinked via arxiv author · 85%Armin Mustafa →
“Listen, Reason, and Segment: Aligning LALMs with Editorial Judgment for Media Chapterization”
- LinkedLinked via arxiv author · 85%Muhammad Awais →
“Listen, Reason, and Segment: Aligning LALMs with Editorial Judgment for Media Chapterization”
- LinkedLinked via arxiv author · 85%Philip J. B. Jackson →
“Listen, Reason, and Segment: Aligning LALMs with Editorial Judgment for Media Chapterization”
- LinkedLinked via arxiv author · 85%Jiankang Deng →
“Listen, Reason, and Segment: Aligning LALMs with Editorial Judgment for Media Chapterization”
- LinkedLinked via arxiv author · 85%Ismail Elezi →
“Listen, Reason, and Segment: Aligning LALMs with Editorial Judgment for Media Chapterization”