Papers with Code 아카이브
Abstractive Text Summarization
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 3
- 평가표
- 20
- 보존된 평가 기록
- 98
Abstractive Text Summarization is the task of generating a short and concise summary that captures the salient ideas of the source text. The generated summaries potentially contain new phrases and sentences that may not appear in the source text.
Source: Generative Adversarial Network for Abstractive Text Summarization
Image credit: Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/coronazap/bert_client
https://github.com/cosmoquester/2021-dialogue-summary-competition
https://github.com/cosmoquester/seq2seq
https://github.com/cosmoquester/transformers-bart-pretrain
https://github.com/cospplay/bert-master
https://github.com/coxy1989/tfmr
https://github.com/cpm0722/transformer_pytorch
https://github.com/ctr4si/MMN
Abstractive Summarization of Reddit Posts with Multi-level Memory Networks
https://github.com/cuber2460/bert
https://github.com/cyberzhg/keras-pos-embd
https://github.com/czero69/acomoeye-NN
https://github.com/d294270681/ProphetNet-paddle
ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training
https://github.com/dadelani/AfriHG
XL-Sum: Large-Scale Multilingual Abstractive Summarization for 44 Languages
https://github.com/danchern97/pointer_generator_pytorch
Get To The Point: Summarization with Pointer-Generator Networks
https://github.com/dannigt/kit-iwslt2023-multilingual
https://github.com/daphne12345/SummarizationRadiologyReports
Get To The Point: Summarization with Pointer-Generator Networks
https://github.com/davidalbertonogueira/NLP-tutorials
https://github.com/davisking/dlib-models
https://github.com/dawn0815/UniSA
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/dccuchile/CC6204
https://github.com/deepmind/haiku
https://github.com/deepmipt/bert/tree/feat/multi_gpu
https://github.com/demdecuong/SEGMENT
https://github.com/demelin/nematode
https://github.com/derylucio/Transformer
https://github.com/dhiraa/tener
https://github.com/dl-nlp/dl-nlp.github.io
https://github.com/dmlc/gluon-nlp
https://github.com/dqqcasia/st
https://github.com/dreamgonfly/Transformer-pytorch
https://github.com/dreamnotover/english_chinese_machine_translation_baseline
https://github.com/drumpt/Transformer
https://github.com/duyunshu/bert-sentiment-analysis
https://github.com/dyq0811/EEG-Transformer-seq2seq
https://github.com/dzqjorking/transpose
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.