https://bitbucket.org/tanya14109/cqasumm
공식 코드CQASUMM: Building References for Community Question Answering Summarization Corpora
Papers with Code 아카이브
Document Summarization
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Automatic Document Summarization is the task of rewriting a document into its shorter form while still retaining its important content. The most popular two paradigms are extractive approaches and abstractive approaches. Extractive approaches generate summaries by extracting parts of the original document (usually sentences), while abstractive methods may generate new words or phrases which are not in the original document.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Alex-Fabbri/Multi-News
공식 코드Multi-News: a Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical Model
https://github.com/AlexGidiotis/DANCER-summ
공식 코드A Divide-and-Conquer Approach to the Summarization of Long Documents
https://github.com/Chen-Wang-CUHK/Training-Free-and-Ref-Free-Summ-Evaluation
공식 코드https://github.com/DhavalTaunk08/XWikiGen
공식 코드XWikiGen: Cross-lingual Summarization for Encyclopedic Text Generation in Low Resource Languages
https://github.com/Diego999/GameWikiSum
공식 코드GameWikiSum: a Novel Large Multi-Document Summarization Dataset
https://github.com/EdinburghNLP/csi-corpus
공식 코드https://github.com/Eulring/GoSum
공식 코드https://github.com/Georgetown-IR-Lab/ExtendedSumm
공식 코드https://github.com/HLTCHKUST/CAiRE-COVID
공식 코드https://github.com/Hellisotherpeople/DebateSum
공식 코드DebateSum: A large-scale argument mining and summarization dataset
https://github.com/Hellisotherpeople/debate2vec
공식 코드DebateSum: A large-scale argument mining and summarization dataset
https://github.com/Hrezaei/SummBot
공식 코드Features in Extractive Supervised Single-document Summarization: Case of Persian News
https://github.com/LCS2-IIITD/summarization_bias
공식 코드Corpora Evaluation and System Bias Detection in Multi-document Summarization
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/Prithwiraj12/Bengali-Deep-News-Summarization
공식 코드Bengali Abstractive News Summarization(BANS): A Neural Attention Approach
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/Laysumm
공식 코드Dimsum @LaySumm 20: BART-based Approach for Scientific Document Summarization
https://github.com/UKPLab/emnlp2017-cmapsum-corpus
공식 코드Bringing Structure into Summaries: Crowdsourcing a Benchmark Corpus of Concept Maps
https://github.com/WING-NUS/scisumm-corpus
공식 코드https://github.com/WoodenWhite/Centrality_Pre_Summ
공식 코드Exploring Explainable Selection to Control Abstractive Summarization
https://github.com/YYF-Tommy/GlobeSumm
공식 코드https://github.com/Yale-LILY/SummerTime
공식 코드https://github.com/ZetangForward/L-CITEEVAL
공식 코드L-CiteEval: Do Long-Context Models Truly Leverage Context for Responding?
https://github.com/ZhangShiyue/EmailSum
공식 코드https://github.com/abisee/pointer-generator
공식 코드Get To The Point: Summarization with Pointer-Generator Networks
https://github.com/adithya7/hybrid-rag
공식 코드Estimating Optimal Context Length for Hybrid Retrieval-augmented Multi-document Summarization
https://github.com/adithya7/scaling-mds
공식 코드Scaling Multi-Document Event Summarization: Evaluating Compression vs. Full-Text Approaches
https://github.com/ai-in-health/clinicbench
공식 코드Large Language Models in the Clinic: A Comprehensive Benchmark
https://github.com/allenai/ms2
공식 코드https://github.com/allenai/mslr-annotated-dataset
공식 코드Automated Metrics for Medical Multi-Document Summarization Disagree with Human Evaluations
https://github.com/allenai/primer
공식 코드PRIMERA: Pyramid-based Masked Sentence Pre-training for Multi-document Summarization
https://github.com/amanuelf/biomed_abstractive_summarization
공식 코드Entity-driven Fact-aware Abstractive Summarization of Biomedical Literature
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.