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Fine-tune BERT for Extractive Summarization

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arxiv 1903.10318 v2 pith:MOHD56TY submitted 2019-03-25 cs.CL

Yang Liu This is my paper · ORCID
classification cs.CL
keywords bertbertsumextractivesummarizationsystemachievedavailablebest-performed
verification ladder T0 review T1 audit T2 compute T3 formal
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BERT, a pre-trained Transformer model, has achieved ground-breaking performance on multiple NLP tasks. In this paper, we describe BERTSUM, a simple variant of BERT, for extractive summarization. Our system is the state of the art on the CNN/Dailymail dataset, outperforming the previous best-performed system by 1.65 on ROUGE-L. The codes to reproduce our results are available at https://github.com/nlpyang/BertSum

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. TalkLess: Blending Extractive and Abstractive Speech Summarization for Editing Speech to Preserve Content and Style

    cs.HC 2025-07 conditional novelty 6.0 of 10

    TalkLess blends extractive and abstractive speech summarization through LLM candidate generation and a weighted scoring function, then converts transcript edits to audio with VoiceCraft, evaluating favorably against a...

  2. HierPromptLM: A Pure PLM-based Framework for Representation Learning on Heterogeneous Text-rich Networks

    cs.LG 2025-01 conditional novelty 6.0 of 10

    HierPromptLM uses hierarchical text prompts and two language-model pretraining tasks to jointly encode node text and heterogeneous graph structure, outperforming prior text-plus-GNN baselines on DBLP and OAG.

  3. AILQA: Evaluating AI-Driven Legal Question Answering Systems for the Indian Legal System

    cs.CL 2026-07 conditional novelty 4.0 of 10

    RAG with top-3 chunk retrieval lifts smaller LLMs on Indian legal QA (Llama2-70B: 45.7% to 51.7% on AIBE) but often hurts large models, and under the study's own rating protocol some AI answers outscored the reference...

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