Text Summarization
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Text Summarization is a natural language processing (NLP) task that involves condensing a lengthy text document into a shorter, more compact version while still retaining the most important information and meaning. The goal is to produce a summary that accurately represents the content of the original text in a concise form.
There are different approaches to text summarization, including extractive methods that identify and extract important sentences or phrases from the text, and abstractive methods that generate new text based on the content of the original text.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/IlyaGusev/summarus
공식 코드Importance of Copying Mechanism for News Headline Generation
https://github.com/InitialBug/BiSET
공식 코드BiSET: Bi-directional Selective Encoding with Template for Abstractive Summarization
https://github.com/JC230903/NLP-low-resource-pipeline-for-indian-languages/tree/main/pythonversion
공식 코드Low-Resource Language Processing: An OCR-Driven Summarization and Translation Pipeline
https://github.com/Jason-Qiu/MultiSum_model
공식 코드MMSum: A Dataset for Multimodal Summarization and Thumbnail Generation of Videos
https://github.com/Javihaus/APE-A-Data-Centric-Benchmark-for-Efficient-LLM-Adaptation-in-Text-Summarization
공식 코드APE: Selective Fine-tuning with Acceptance Criteria for Language Model Adaptation
https://github.com/JbRemy/Cahan
공식 코드Bidirectional Context-Aware Hierarchical Attention Network for Document Understanding
https://github.com/Jxu-Thu/DITTO
공식 코드Learning to Break the Loop: Analyzing and Mitigating Repetitions for Neural Text Generation
https://github.com/KaiQiangSong/struct_infused_summ
공식 코드Structure-Infused Copy Mechanisms for Abstractive Summarization
https://github.com/LCS2-IIITD/summarization_bias
공식 코드Corpora Evaluation and System Bias Detection in Multi-document Summarization
https://github.com/LaraQianYang/Ouroboros
공식 코드Ouroboros: On Accelerating Training of Transformer-Based Language Models
https://github.com/Law-AI/DELSumm
공식 코드Incorporating Domain Knowledge for Extractive Summarization of Legal Case Documents
https://github.com/LiqunChen0606/Graph-Optimal-Transport
공식 코드https://github.com/LucaSantagata/More-is-More-Addition-Bias-in-Large-Language-Models
공식 코드https://github.com/MiuLab/DialSum
공식 코드Abstractive Dialogue Summarization with Sentence-Gated Modeling Optimized by Dialogue Acts
https://github.com/NUSTM/EMDRC
공식 코드Towards Explainable Multimodal Depression Recognition for Clinical Interviews
https://github.com/NamanChhibbar/Long-Document-Summarizer
공식 코드https://github.com/OnlpLab/HeSum
공식 코드HeSum: a Novel Dataset for Abstractive Text Summarization in Hebrew
https://github.com/PaddlePaddle/Research
공식 코드Leveraging Graph to Improve Abstractive Multi-Document Summarization
https://github.com/PortNLP/FairEXTSummarizer
공식 코드Fair Summarization: Bridging Quality and Diversity in Extractive Summaries
https://github.com/PrekshaNema25/DiverstiyBasedAttentionMechanism
공식 코드Diversity driven Attention Model for Query-based Abstractive Summarization
https://github.com/Priberam/exconsumm
공식 코드Jointly Extracting and Compressing Documents with Summary State Representations
https://github.com/Prithwiraj12/Bengali-Deep-News-Summarization
공식 코드Bengali Abstractive News Summarization(BANS): A Neural Attention Approach
https://github.com/RUCAIBox/TextBox
공식 코드TextBox 2.0: A Text Generation Library with Pre-trained Language Models
https://github.com/SeungoneKim/SICK_Summarization
공식 코드Mind the Gap! Injecting Commonsense Knowledge for Abstractive Dialogue Summarization
https://github.com/SofeeyaJ/GASCADE_ECIR2025
공식 코드GASCADE: Grouped Summarization of Adverse Drug Event for Enhanced Cancer Pharmacovigilance
https://github.com/Stareru/NeighboringDistributionDivergence
공식 코드Contextualized Semantic Distance between Highly Overlapped Texts
https://github.com/StevenSong/multimodal-cancer-survival
공식 코드Multimodal Survival Modeling in the Age of Foundation Models
https://github.com/SunbowLiu/SurfaceFusion
공식 코드Understanding and Improving Encoder Layer Fusion in Sequence-to-Sequence Learning
https://github.com/THU-KEG/TWAG
공식 코드https://github.com/THUDM/GLM
공식 코드GLM: General Language Model Pretraining with Autoregressive Blank Infilling
https://github.com/THUNLP-MT/TRICE
공식 코드Transfer Learning for Sequence Generation: from Single-source to Multi-source
https://github.com/TysonYu/AdaptSum
공식 코드AdaptSum: Towards Low-Resource Domain Adaptation for Abstractive Summarization
https://github.com/TysonYu/Laysumm
공식 코드Dimsum @LaySumm 20: BART-based Approach for Scientific Document Summarization
https://github.com/UKPLab/arxiv2021-evaluation-discrepancy-nsc
공식 코드Evaluation Discrepancy Discovery: A Sentence Compression Case-study
https://github.com/UKPLab/emnlp2017-cmapsum-corpus
공식 코드Bringing Structure into Summaries: Crowdsourcing a Benchmark Corpus of Concept Maps
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.