# From Text Segmentation to Smart Chaptering: A Novel Benchmark for Structuring Video Transcriptions

arXiv: https://arxiv.org/abs/2402.17633 · HTML: https://mlsanity.com/paper/2402.17633/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 43 references that are in the map.
- First posted on arXiv 27 Feb 2024.

## Builds on (most cited first, 25 of 25)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://mlsanity.com/paper/2104.09864/) (2021)
- [MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers](https://mlsanity.com/paper/2002.10957/) (2020)
- [BARTScore: Evaluating Generated Text as Text Generation](https://mlsanity.com/paper/2106.11520/) (2021)
- [QMSum: A New Benchmark for Query-based Multi-domain Meeting Summarization](https://mlsanity.com/paper/2104.05938/) (2021)
- [One Embedder, Any Task: Instruction-Finetuned Text Embeddings](https://mlsanity.com/paper/2212.09741/) (2022)
- [Text Segmentation by Cross Segment Attention](https://mlsanity.com/paper/2004.14535/) (2020)
- [Generating Representative Headlines for News Stories](https://mlsanity.com/paper/2001.09386/) (2020)
- [Toward Unifying Text Segmentation and Long Document Summarization](https://mlsanity.com/paper/2210.16422/) (2022)
- [Title Generation for User Generated Videos](https://mlsanity.com/paper/1608.07068/) (2016)
- [End-to-End Segmentation-based News Summarization](https://mlsanity.com/paper/2110.07850/) (2021)
- [Outline Generation: Understanding the Inherent Content Structure of Documents](https://mlsanity.com/paper/1905.10039/) (2019)
- [Diverse, Controllable, and Keyphrase-Aware: A Corpus and Method for News Multi-Headline Generation](https://mlsanity.com/paper/2004.03875/) (2020)
- [VT-SSum: A Benchmark Dataset for Video Transcript Segmentation and Summarization](https://mlsanity.com/paper/2106.05606/) (2021)
- [The Current State of Summarization](https://mlsanity.com/paper/2305.04853/) (2023)
- [Improving Stack Overflow question title generation with copying enhanced CodeBERT model and bi-modal information](https://mlsanity.com/paper/2109.13073/) (2021)
- [Topic Segmentation in the Wild: Towards Segmentation of Semi-structured & Unstructured Chats](https://mlsanity.com/paper/2211.14954/) (2022)
- [AutoPRTitle: A Tool for Automatic Pull Request Title Generation](https://mlsanity.com/paper/2206.11619/) (2022)
- [Diverse Title Generation for Stack Overflow Posts with Multiple Sampling Enhanced Transformer](https://mlsanity.com/paper/2208.11523/) (2022)
- [Multimodal Prompt Learning for Product Title Generation with Extremely Limited Labels](https://mlsanity.com/paper/2307.01969/) (2023)
- [TCR: Short Video Title Generation and Cover Selection with Attention Refinement](https://mlsanity.com/paper/2304.12561/) (2023)

## Built on by (most cited first, 16 of 16)

- [Chapter-Llama: Efficient Chaptering in Hour-Long Videos with LLMs](https://mlsanity.com/paper/2504.00072/) (2025)
- [PODTILE: Facilitating Podcast Episode Browsing with Auto-generated Chapters](https://mlsanity.com/paper/2410.16148/) (2024)
- [Cocktail-Party Audio-Visual Speech Recognition](https://mlsanity.com/paper/2506.02178/) (2025)
- [Large Language Models for Page Stream Segmentation](https://mlsanity.com/paper/2408.11981/) (2024)
- [Summarizing Speech: A Comprehensive Survey](https://mlsanity.com/paper/2504.08024/) (2025)
- [Towards Multi-Level Transcript Segmentation: LoRA Fine-Tuning for Table-of-Contents Generation](https://mlsanity.com/paper/2601.02128/) (2026)
- [Beyond Transcripts: A Renewed Perspective on Audio Chaptering](https://mlsanity.com/paper/2602.08979/) (2026)
- [Paragraph Segmentation Revisited: Towards a Standard Task for Structuring Speech](https://mlsanity.com/paper/2512.24517/) (2025)
- [The AI Co-Ethnographer: How Far Can Automation Take Qualitative Research?](https://mlsanity.com/paper/2505.00012/) (2025)
- [Reading Between the Waves: Robust Topic Segmentation Using Inter-Sentence Audio Features](https://mlsanity.com/paper/2602.06647/) (2026)
- [Do What I Say: A Spoken Prompt Dataset for Instruction-Following](https://mlsanity.com/paper/2603.09881/) (2026)
- [Linear Semantic Segmentation for Low-Resource Spoken Dialects](https://mlsanity.com/paper/2605.06276/) (2026)
- [Multilingual Long-Form Speech Instruction Following: KIT's Submission to IWSLT 2026](https://mlsanity.com/paper/2606.04730/) (2026)
- [FBK's Long-form SpeechLLMs for IWSLT 2026 Instruction Following](https://mlsanity.com/paper/2606.26819/) (2026)
- [Reusing Latent Speech Representations for Query-Conditioned Topic Localization in Transcripts](https://mlsanity.com/paper/2609.21844/) (2026)
- [Zero-Shot Cue-Grounded Topic Segmentation of Spoken Documents](https://mlsanity.com/paper/2609.34425/) (2026)

## Abstract (arXiv)

Text segmentation is a fundamental task in natural language processing, where documents are split into contiguous sections. However, prior research in this area has been constrained by limited datasets, which are either small in scale, synthesized, or only contain well-structured documents. In this paper, we address these limitations by introducing a novel benchmark YTSeg focusing on spoken content that is inherently more unstructured and both topically and structurally diverse. As part of this work, we introduce an efficient hierarchical segmentation model MiniSeg, that outperforms state-of-the-art baselines. Lastly, we expand the notion of text segmentation to a more practical "smart chaptering" task that involves the segmentation of unstructured content, the generation of meaningful segment titles, and a potential real-time application of the models.