# Leveraging BERT for Extractive Text Summarization on Lectures

arXiv: https://arxiv.org/abs/1906.04165 · HTML: https://mlsanity.com/paper/1906.04165/

## Key facts

- Cited by 63 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jun 2019.

## Built on by (most cited first, 30 of 63)

- [BERT: A Review of Applications in Natural Language Processing and Understanding](https://mlsanity.com/paper/2103.11943/) (2021)
- [Do Language Models Plagiarize?](https://mlsanity.com/paper/2203.07618/) (2022)
- [Align and Attend: Multimodal Summarization with Dual Contrastive Losses](https://mlsanity.com/paper/2303.07284/) (2023)
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- [Multi-LexSum: Real-World Summaries of Civil Rights Lawsuits at Multiple Granularities](https://mlsanity.com/paper/2206.10883/) (2022)
- [LeanContext: Cost-Efficient Domain-Specific Question Answering Using LLMs](https://mlsanity.com/paper/2309.00841/) (2023)
- [ConvoSumm: Conversation Summarization Benchmark and Improved Abstractive Summarization with Argument Mining](https://mlsanity.com/paper/2106.00829/) (2021)
- [PriMock57: A Dataset Of Primary Care Mock Consultations](https://mlsanity.com/paper/2204.00333/) (2022)
- [Text Clustering with Large Language Model Embeddings](https://mlsanity.com/paper/2403.15112/) (2024)
- [ArgLegalSumm: Improving Abstractive Summarization of Legal Documents with Argument Mining](https://mlsanity.com/paper/2209.01650/) (2022)
- [SPEC5G: A Dataset for 5G Cellular Network Protocol Analysis](https://mlsanity.com/paper/2301.09201/) (2023)
- [Attention-based Clinical Note Summarization](https://mlsanity.com/paper/2104.08942/) (2021)
- [Collaborative Filtering with Attribution Alignment for Review-based Non-overlapped Cross Domain Recommendation](https://mlsanity.com/paper/2202.04920/) (2022)
- [Memory and Knowledge Augmented Language Models for Inferring Salience in Long-Form Stories](https://mlsanity.com/paper/2109.03754/) (2021)
- [A Review of the Trends and Challenges in Adopting Natural Language Processing Methods for Education Feedback Analysis](https://mlsanity.com/paper/2301.08826/) (2023)
- [MMSum: A Dataset for Multimodal Summarization and Thumbnail Generation of Videos](https://mlsanity.com/paper/2306.04216/) (2023)
- [DepressionNet: A Novel Summarization Boosted Deep Framework for Depression Detection on Social Media](https://mlsanity.com/paper/2105.10878/) (2021)
- [An Evaluation Framework for Legal Document Summarization](https://mlsanity.com/paper/2205.08478/) (2022)
- [Prompt Valuation Based on Shapley Values](https://mlsanity.com/paper/2312.15395/) (2023)
- [Multi-level Fusion of Wav2vec 2.0 and BERT for Multimodal Emotion Recognition](https://mlsanity.com/paper/2207.04697/) (2022)
- [Key-Element-Informed sLLM Tuning for Document Summarization](https://mlsanity.com/paper/2406.04625/) (2024)
- [Nutri-bullets: Summarizing Health Studies by Composing Segments](https://mlsanity.com/paper/2103.11921/) (2021)
- [SlideSpawn: An Automatic Slides Generation System for Research Publications](https://mlsanity.com/paper/2411.17719/) (2024)
- [Towards objectively evaluating the quality of generated medical summaries](https://mlsanity.com/paper/2104.04412/) (2021)
- [Summarizing Speech: A Comprehensive Survey](https://mlsanity.com/paper/2504.08024/) (2025)
- [NLPBK at VLSP-2020 shared task: Compose transformer pretrained models for Reliable Intelligence Identification on Social network](https://mlsanity.com/paper/2101.12672/) (2021)
- [Contextual Networks and Unsupervised Ranking of Sentences](https://mlsanity.com/paper/2203.04459/) (2022)
- [Applying Automatic Text Summarization for Fake News Detection](https://mlsanity.com/paper/2204.01841/) (2022)
- [Argumentative Segmentation Enhancement for Legal Summarization](https://mlsanity.com/paper/2307.05081/) (2023)
- [Understanding Position Bias Effects on Fairness in Social Multi-Document Summarization](https://mlsanity.com/paper/2405.01790/) (2024)

## Abstract (arXiv)

In the last two decades, automatic extractive text summarization on lectures has demonstrated to be a useful tool for collecting key phrases and sentences that best represent the content. However, many current approaches utilize dated approaches, producing sub-par outputs or requiring several hours of manual tuning to produce meaningful results. Recently, new machine learning architectures have provided mechanisms for extractive summarization through the clustering of output embeddings from deep learning models. This paper reports on the project called Lecture Summarization Service, a python based RESTful service that utilizes the BERT model for text embeddings and KMeans clustering to identify sentences closes to the centroid for summary selection. The purpose of the service was to provide students a utility that could summarize lecture content, based on their desired number of sentences. On top of the summary work, the service also includes lecture and summary management, storing content on the cloud which can be used for collaboration. While the results of utilizing BERT for extractive summarization were promising, there were still areas where the model struggled, providing feature research opportunities for further improvement.