https://github.com/Alex-Fabbri/Multi-News
공식 코드Multi-News: a Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical Model
Papers with Code 아카이브
Extractive Summarization
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/DhavalTaunk08/XWikiGen
공식 코드XWikiGen: Cross-lingual Summarization for Encyclopedic Text Generation in Low Resource Languages
https://github.com/EdinburghNLP/csi-corpus
공식 코드https://github.com/Espresso-AI/OrderSum
공식 코드OrderSum: Semantic Sentence Ordering for Extractive Summarization
https://github.com/Eulring/GoSum
공식 코드https://github.com/FMOpee/WGSS
공식 코드https://github.com/Georgetown-IR-Lab/ExtendedSumm
공식 코드https://github.com/Hellisotherpeople/CX_DB8
공식 코드CX DB8: A queryable extractive summarizer and semantic search engine
https://github.com/JetBrains-Research/pubtrends-review
공식 코드https://github.com/JohnMBrandt/text-classification
공식 코드Imbalanced multi-label classification using multi-task learning with extractive summarization
https://github.com/Law-AI/DELSumm
공식 코드Incorporating Domain Knowledge for Extractive Summarization of Legal Case Documents
https://github.com/NeelKanwal/BERTOLOGY-Based-Extractive-Summarization-for-Clinical-Notes
공식 코드https://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/Ruifeng-paper/FactExsum-coling2020
공식 코드Fact-level Extractive Summarization with Hierarchical Graph Mask on BERT
https://github.com/Tswings/AVeriTeC-DCE
공식 코드Document-level Claim Extraction and Decontextualisation for Fact-Checking
https://github.com/Wendy-Xiao/Extsumm_local_global_context
공식 코드Extractive Summarization of Long Documents by Combining Global and Local Context
https://github.com/acpotluri/lfqa_summary
공식 코드Concise Answers to Complex Questions: Summarization of Long-form Answers
https://github.com/ad93/FairSumm
공식 코드https://github.com/ai4society/trustworthy-chatbot
공식 코드https://github.com/alibaba-damo-academy/spokennlp
공식 코드MUG: A General Meeting Understanding and Generation Benchmark
https://github.com/amagooda/SummaRuNNer_coattention
공식 코드Attend to the beginning: A study on using bidirectional attention for extractive summarization
https://github.com/amirarcane/bertopic
공식 코드https://github.com/asappresearch/clip
공식 코드CLIP: A Dataset for Extracting Action Items for Physicians from Hospital Discharge Notes
https://github.com/atharsefid/Extractive_Research_Slide_Generation_Using_Windowed_Labeling_Ranking
공식 코드Extractive Research Slide Generation Using Windowed Labeling Ranking
https://github.com/atharsefid/scibertsum
공식 코드SciBERTSUM: Extractive Summarization for Scientific Documents
https://github.com/baoguangsheng/ctx-rewriter-for-summ
공식 코드https://github.com/bauerem/legal_memsum
공식 코드https://github.com/botondbarta/hunsum
공식 코드From News to Summaries: Building a Hungarian Corpus for Extractive and Abstractive Summarization
https://github.com/brcsomnath/coversumm
공식 코드Incremental Extractive Opinion Summarization Using Cover Trees
https://github.com/brcsomnath/semae
공식 코드Unsupervised Extractive Opinion Summarization Using Sparse Coding
https://github.com/brxx122/HeterSUMGraph
공식 코드Heterogeneous Graph Neural Networks for Extractive Document Summarization
https://github.com/chz816/tas
공식 코드Topic-Guided Abstractive Text Summarization: a Joint Learning Approach
https://github.com/computationalmedia/supmmd
공식 코드SupMMD: A Sentence Importance Model for Extractive Summarization using Maximum Mean Discrepancy
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.