REVIEW 2 cited by
Fine-tune BERT for Extractive Summarization
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
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
Forward citations
Cited by 2 Pith papers
-
TalkLess: Blending Extractive and Abstractive Speech Summarization for Editing Speech to Preserve Content and Style
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...
-
AILQA: Evaluating AI-Driven Legal Question Answering Systems for the Indian Legal System
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...
Discussion (0). Continue with ORCID to comment.