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Abstractive Text Summarization
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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.
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https://github.com/JohannLee1996/bert
https://github.com/Joshuaoneheart/Fintech-Final-Report
https://github.com/JudeLee19/HMNet
A Hierarchical Network for Abstractive Meeting Summarization with Cross-Domain Pretraining
https://github.com/JudeLee19/HMNet-End-to-End-Abstractive-Summarization-for-Meetings
A Hierarchical Network for Abstractive Meeting Summarization with Cross-Domain Pretraining
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Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
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Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
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https://github.com/Kevin-Vora/bert-embedding-gluonnlp-edit-
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Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
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Unified Language Model Pre-training for Natural Language Understanding and Generation
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https://github.com/Kumar-Tarun/summarization
Get To The Point: Summarization with Pointer-Generator Networks
https://github.com/KushGrandhi/Polaroid
https://github.com/Kyubyong/transformer
https://github.com/Lemoners/Attention-Is-All-You-Need
https://github.com/LeoLaugier/conditional-auto-encoder-text-to-text-transfer-transformer
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
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https://github.com/MS-Mind/MS-Code-01/tree/main/seq2seq
Lossless Acceleration for Seq2seq Generation with Aggressive Decoding
https://github.com/MS-P3/code5/tree/main/t5
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/MS-P3/code7/tree/main/xlm_prophetnet
ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training
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https://github.com/ManuMahadevaswamy/PEGASUS
PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.