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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.
https://github.com/hannibal046/selfmemory
공식 코드Lift Yourself Up: Retrieval-augmented Text Generation with Self Memory
https://github.com/helmertz/querysum
공식 코드https://github.com/helmertz/querysum-data
공식 코드https://github.com/hemingkx/gad
공식 코드Speculative Decoding: Exploiting Speculative Execution for Accelerating Seq2seq Generation
https://github.com/hemingkx/specdec
공식 코드Speculative Decoding: Exploiting Speculative Execution for Accelerating Seq2seq Generation
https://github.com/hkust-knowcomp/knowledge-constrained-decoding
공식 코드KCTS: Knowledge-Constrained Tree Search Decoding with Token-Level Hallucination Detection
https://github.com/hltchkust/vg-gplms
공식 코드Vision Guided Generative Pre-trained Language Models for Multimodal Abstractive Summarization
https://github.com/hooshvare/pn-summary
공식 코드Leveraging ParsBERT and Pretrained mT5 for Persian Abstractive Text Summarization
https://github.com/hpzhang94/DomainSum
공식 코드DomainSum: A Hierarchical Benchmark for Fine-Grained Domain Shift in Abstractive Text Summarization
https://github.com/huankoh/How-Far-are-We-from-Robust-Long-Abstractive-Summarization
공식 코드https://github.com/hwanheelee1993/mfma
공식 코드https://github.com/iammia0o/EMO-KNOW
공식 코드EMO-KNOW: A Large Scale Dataset on Emotion and Emotion-cause
https://github.com/icml-2020-nlp/semsim
공식 코드Learning by Semantic Similarity Makes Abstractive Summarization Better
https://github.com/ict-bigdatalab/ci-seq2seq
공식 코드Inducing Causal Structure for Abstractive Text Summarization
https://github.com/idiap/translation-aided-slu
공식 코드https://github.com/instance-wise-ordered-transformer/IOT
공식 코드IOT: Instance-wise Layer Reordering for Transformer Structures
https://github.com/iriscxy/fes
공식 코드https://github.com/iriscxy/unified-timeline-summarizer
공식 코드Follow the Timeline! Generating Abstractive and Extractive Timeline Summary in Chronological Order
https://github.com/ixlan/CopyCat-abstractive-opinion-summarizer
공식 코드Unsupervised Opinion Summarization as Copycat-Review Generation
https://github.com/jacobkrantz/VertMetric
공식 코드https://github.com/janetlauyeung/gumsum4eval
공식 코드GUMSum: Multi-Genre Data and Evaluation for English Abstractive Summarization
https://github.com/jantrienes/inlg2023-radsum
공식 코드Guidance in Radiology Report Summarization: An Empirical Evaluation and Error Analysis
https://github.com/jeewoo1025/balsum
공식 코드Balancing Lexical and Semantic Quality in Abstractive Summarization
https://github.com/jiacheng-xu/sum-interpret
공식 코드Dissecting Generation Modes for Abstractive Summarization Models via Ablation and Attribution
https://github.com/jiacheng-xu/text-sum-uncertainty
공식 코드Understanding Neural Abstractive Summarization Models via Uncertainty
https://github.com/jiangycTarheel/TPT-Summ
공식 코드Enriching Transformers with Structured Tensor-Product Representations for Abstractive Summarization
https://github.com/jordiclive/ControlPrefixes
공식 코드https://github.com/joshbambrick/falsesum
공식 코드https://github.com/kaushal0494/meta_xnlg
공식 코드https://github.com/kohilin/ealm
공식 코드Q-learning with Language Model for Edit-based Unsupervised Summarization
https://github.com/kukrishna/usb
공식 코드USB: A Unified Summarization Benchmark Across Tasks and Domains
https://github.com/l3cube-pune/MarathiNLP
공식 코드https://github.com/lancopku/Global-Encoding
공식 코드https://github.com/lancopku/WEAN
공식 코드https://github.com/lancopku/superAE
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.