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://bitbucket.org/tanya14109/cqasumm
공식 코드CQASUMM: Building References for Community Question Answering Summarization Corpora
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/Ali-Forootani/multi_llm
공식 코드Bio-Eng-LMM AI Assist chatbot: A Comprehensive Tool for Research and Education
https://github.com/Alibaba-NLP/CHRONOS
공식 코드Unfolding the Headline: Iterative Self-Questioning for News Retrieval and Timeline Summarization
https://github.com/Andrew03/transformer-abstractive-summarization
공식 코드Efficient Adaptation of Pretrained Transformers for Abstractive Summarization
https://github.com/ArrowHuang/NumHG
공식 코드https://github.com/Babelscape/FENICE
공식 코드https://github.com/BinWang28/InstructDS
공식 코드https://github.com/CZWin32768/xnlg
공식 코드https://github.com/Chen-Wang-CUHK/Training-Free-and-Ref-Free-Summ-Evaluation
공식 코드https://github.com/ChenRocks/Distill-BERT-Textgen
공식 코드https://github.com/ChenRocks/fast_abs_rl
공식 코드Fast Abstractive Summarization with Reinforce-Selected Sentence Rewriting
https://github.com/ChenxinAn-fdu/CGSum
공식 코드Enhancing Scientific Papers Summarization with Citation Graph
https://github.com/CyberAgentAILab/adaptive-mbr
공식 코드Hyperparameter-Free Approach for Faster Minimum Bayes Risk Decoding
https://github.com/DISL-Lab/FineSurE
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/Dod-o/VT-SSum
공식 코드VT-SSum: A Benchmark Dataset for Video Transcript Segmentation and Summarization
https://github.com/EdinburghNLP/csi-corpus
공식 코드https://github.com/Eulring/GoSum
공식 코드https://github.com/FMOpee/WGSS
공식 코드https://github.com/FiscalNote/BillSum
공식 코드BillSum: A Corpus for Automatic Summarization of US Legislation
https://github.com/GT-SALT/Multi-View-Seq2Seq
공식 코드https://github.com/Georgetown-IR-Lab/ExtendedSumm
공식 코드https://github.com/HLTCHKUST/CAiRE-COVID
공식 코드https://github.com/HLTCHKUST/QFS
공식 코드Improve Query Focused Abstractive Summarization by Incorporating Answer Relevance
https://github.com/HadiAskari/Relevance-Paraphrasing
공식 코드Assessing LLMs for Zero-shot Abstractive Summarization Through the Lens of Relevance Paraphrasing
https://github.com/Hellisotherpeople/CX_DB8
공식 코드CX DB8: A queryable extractive summarizer and semantic search engine
https://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/IBM/SPAHM
공식 코드https://github.com/IBM/document2slides
공식 코드D2S: Document-to-Slide Generation Via Query-Based Text Summarization
https://github.com/IbrahimAlAzhar/LimTopic
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.