Fine-tune BERT for Extractive Summarization

03/25/2019
by   Yang Liu, et al.
0

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

READ FULL TEXT

page 1

page 2

page 3

page 4

research
12/19/2019

BERTje: A Dutch BERT Model

The transformer-based pre-trained language model BERT has helped to impr...
research
01/13/2019

Passage Re-ranking with BERT

Recently, neural models pretrained on a language modeling task, such as ...
research
01/24/2023

Model soups to increase inference without increasing compute time

In this paper, we compare Model Soups performances on three different mo...
research
04/27/2020

DeeBERT: Dynamic Early Exiting for Accelerating BERT Inference

Large-scale pre-trained language models such as BERT have brought signif...
research
06/16/2021

TSSuBERT: Tweet Stream Summarization Using BERT

The development of deep neural networks and the emergence of pre-trained...
research
05/23/2023

All Roads Lead to Rome? Exploring the Invariance of Transformers' Representations

Transformer models bring propelling advances in various NLP tasks, thus ...
research
02/12/2022

Indication as Prior Knowledge for Multimodal Disease Classification in Chest Radiographs with Transformers

When a clinician refers a patient for an imaging exam, they include the ...

Please sign up or login with your details

Forgot password? Click here to reset