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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/ntunlp/summareranker
공식 코드SummaReranker: A Multi-Task Mixture-of-Experts Re-ranking Framework for Abstractive Summarization
https://github.com/nusnlp/prs
공식 코드Preference-Guided Reflective Sampling for Aligning Language Models
https://github.com/nyu-mll/squality
공식 코드SQuALITY: Building a Long-Document Summarization Dataset the Hard Way
https://github.com/oaimli/hgsum
공식 코드Compressed Heterogeneous Graph for Abstractive Multi-Document Summarization
https://github.com/oaimli/peersum
공식 코드PeerSum: A Peer Review Dataset for Abstractive Multi-document Summarization
https://github.com/odelliab/HowSumm
공식 코드HowSumm: A Multi-Document Summarization Dataset Derived from WikiHow Articles
https://github.com/oriern/SPARK
공식 코드https://github.com/oriern/SuperPAL
공식 코드Summary-Source Proposition-level Alignment: Task, Datasets and Supervised Baseline
https://github.com/oriern/clusterprop
공식 코드Proposition-Level Clustering for Multi-Document Summarization
https://github.com/oriern/procluster
공식 코드Proposition-Level Clustering for Multi-Document Summarization
https://github.com/oswald1997/glimmer
공식 코드GLIMMER: Incorporating Graph and Lexical Features in Unsupervised Multi-Document Summarization
https://github.com/paper-submission-rafa/diversification-code
공식 코드Result Diversification by Multi-objective Evolutionary Algorithms with Theoretical Guarantees
https://github.com/pku-ml/longppl
공식 코드What is Wrong with Perplexity for Long-context Language Modeling?
https://github.com/potsawee/longsum0
공식 코드Long-Span Summarization via Local Attention and Content Selection
https://github.com/ppapalampidi/SUMMER
공식 코드https://github.com/priberam/cera-summ
공식 코드Supervising the Centroid Baseline for Extractive Multi-Document Summarization
https://github.com/psunlpgroup/summ-n
공식 코드Summ^N: A Multi-Stage Summarization Framework for Long Input Dialogues and Documents
https://github.com/pytorch/fairseq
공식 코드Pay Less Attention with Lightweight and Dynamic Convolutions
https://github.com/rajdeep345/ControllableSumm
공식 코드Read what you need: Controllable Aspect-based Opinion Summarization of Tourist Reviews
https://github.com/ramakanth-pasunuru/QmdsCnnIr
공식 코드Data Augmentation for Abstractive Query-Focused Multi-Document Summarization
https://github.com/ratishsp/centrum
공식 코드Multi-Document Summarization with Centroid-Based Pretraining
https://github.com/ryanzhumich/AESLC
공식 코드This Email Could Save Your Life: Introducing the Task of Email Subject Line Generation
https://github.com/saketdingliwal/Abstractive-Dialogue-Summarization
공식 코드https://github.com/salesforce/diversesumm
공식 코드https://github.com/saxenarohit/moviesum
공식 코드MovieSum: An Abstractive Summarization Dataset for Movie Screenplays
https://github.com/scsmuhio/summarization
공식 코드GAE-ISumm: Unsupervised Graph-Based Summarization of Indian Languages
https://github.com/sebastianGehrmann/bottom-up-summary
공식 코드https://github.com/shashiongithub/Refresh
공식 코드Ranking Sentences for Extractive Summarization with Reinforcement Learning
https://github.com/shashiongithub/XSum
공식 코드https://github.com/shashiongithub/sidenet
공식 코드https://github.com/snowflakedb/ArcticTraining
공식 코드Arctic Long Sequence Training: Scalable And Efficient Training For Multi-Million Token Sequences
https://github.com/spcl/checkembed
공식 코드CheckEmbed: Effective Verification of LLM Solutions to Open-Ended Tasks
https://github.com/stevenlau6/bigsurvey
공식 코드Generating a Structured Summary of Numerous Academic Papers: Dataset and Method
https://github.com/stevenlau6/findsum
공식 코드https://github.com/tagoyal/snac
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.