Papers with Code 아카이브

Extractive Summarization

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

0

평가표

0

보존된 평가 기록

0

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/kylehiroyasu/bert-extractive-summarizer

Leveraging BERT for Extractive Text Summarization on Lectures

https://github.com/law-ai/summarization

Extractive Summarization using Deep Learning

https://github.com/lingyu001/nlp_text_summarization_implementation

Fine-tune BERT for Extractive Summarization

https://github.com/lucidrains/memory-compressed-attention

Generating Wikipedia by Summarizing Long Sequences

https://github.com/magic282/NeuSum

Neural Document Summarization by Jointly Learning to Score and Select Sentences

https://github.com/maheshmylavarapu0057/QueryBasedSummarisation

Diversity driven Attention Model for Query-based Abstractive Summarization

https://github.com/maszhongming/Effective_Extractive_Summarization

Searching for Effective Neural Extractive Summarization: What Works and What's Next

https://github.com/minsoo9506/NLP-study

Self-Supervised Learning for Contextualized Extractive Summarization

https://github.com/mirandrom/HipoRank

Discourse-Aware Unsupervised Summarization of Long Scientific Documents

https://github.com/muazhari/semantic-search

CX DB8: A queryable extractive summarizer and semantic search engine

https://github.com/nakhunchumpolsathien/TR-TPBS

Fine-tune BERT for Extractive Summarization

https://github.com/nakhunchumpolsathien/ThaiSum

AREDSUM: Adaptive Redundancy-Aware Iterative Sentence Ranking for Extractive Document Summarization

https://github.com/nguyenphamvan/BertSum-master

Fine-tune BERT for Extractive Summarization

https://github.com/no-execution/Summa_label

SummaRuNNer: A Recurrent Neural Network based Sequence Model for Extractive Summarization of Documents

https://github.com/raqoon886/KoBertSum

Fine-tune BERT for Extractive Summarization

https://github.com/raqoon886/KorBertSum

Fine-tune BERT for Extractive Summarization

https://github.com/saranthn/FYP-Bert

Leveraging BERT for Extractive Text Summarization on Lectures

https://github.com/seongminp/hdseg

Unsupervised Extractive Dialogue Summarization in Hyperdimensional Space

https://github.com/shivampanjeta/bert-extractive-summarizer

Leveraging BERT for Extractive Text Summarization on Lectures

https://github.com/tensorflow/models/tree/master/research/syntaxnet

DRAGNN: A Transition-based Framework for Dynamically Connected Neural Networks

https://github.com/thangarani/bertsum

Fine-tune BERT for Extractive Summarization

https://github.com/vsubramaniam851/typ_embed

Fine-tune BERT for Extractive Summarization

https://github.com/zhdbwe/Paper-DailyReading

Searching for Effective Neural Extractive Summarization: What Works and What's Next

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.