Papers with Code 아카이브
Text Summarization
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 12
- 평가표
- 37
- 보존된 평가 기록
- 233
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/tomhosking/hiro
공식 코드Hierarchical Indexing for Retrieval-Augmented Opinion Summarization
https://github.com/tongbao96/code-for-sfr-as
공식 코드Enhancing Abstractive Summarization of Scientific Papers Using Structure Information
https://github.com/tonmoytalukder/rank-your-summaries-enhancing-bengali-text-summarization-via-ranking-based-approach
공식 코드Rank Your Summaries: Enhancing Bengali Text Summarization via Ranking-based Approach
https://github.com/tot98git/text-summ
공식 코드https://github.com/travel-go/Abstractive-Text-Summarization
공식 코드Contrastive Attention Mechanism for Abstractive Sentence Summarization
https://github.com/tristanratz/scu-text-evaluation
공식 코드On the Role of Summary Content Units in Text Summarization Evaluation
https://github.com/trustairlab/synthetic_artifact_auditing
공식 코드Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications
https://github.com/tshi04/LeafNATS
공식 코드LeafNATS: An Open-Source Toolkit and Live Demo System for Neural Abstractive Text Summarization
https://github.com/tshi04/NATS
공식 코드Neural Abstractive Text Summarization with Sequence-to-Sequence Models
https://github.com/tsosea2/covidet-ext
공식 코드https://github.com/ucfnlp/cascaded-summ
공식 코드A Cascade Approach to Neural Abstractive Summarization with Content Selection and Fusion
https://github.com/ucfnlp/control-over-copying
공식 코드Controlling the Amount of Verbatim Copying in Abstractive Summarization
https://github.com/ucfnlp/endorser-summ
공식 코드Modeling Endorsement for Multi-Document Abstractive Summarization
https://github.com/ucfnlp/joint-parse-n-summarize
공식 코드https://github.com/ucfnlp/multidoc_summarization
공식 코드Adapting the Neural Encoder-Decoder Framework from Single to Multi-Document Summarization
https://github.com/ucfnlp/points-of-correspondence
공식 코드Understanding Points of Correspondence between Sentences for Abstractive Summarization
https://github.com/ucfnlp/summarization-dpp-capsnet
공식 코드https://github.com/ucfnlp/summarization-sing-pair-mix
공식 코드Scoring Sentence Singletons and Pairs for Abstractive Summarization
https://github.com/ucfnlp/varying-length-summ
공식 코드A New Approach to Overgenerating and Scoring Abstractive Summaries
https://github.com/ucrel/welsh-summarization-dataset
공식 코드Introducing the Welsh Text Summarisation Dataset and Baseline Systems
https://github.com/utnslab/dsla-serve
공식 코드On-the-Fly Adaptive Distillation of Transformer to Dual-State Linear Attention
https://github.com/utsjiyaoli/qa-attack
공식 코드Deceiving Question-Answering Models: A Hybrid Word-Level Adversarial Approach
https://github.com/vagisha-nidhi/TextSummarizer
공식 코드https://github.com/valdecy/pybibx
공식 코드https://github.com/vanderpoelliam/cpmi
공식 코드Mutual Information Alleviates Hallucinations in Abstractive Summarization
https://github.com/varadhbhatnagar/fc-claim-det
공식 코드Harnessing Abstractive Summarization for Fact-Checked Claim Detection
https://github.com/varal7/conformal-language-modeling
공식 코드https://github.com/vidhishanair/factedit
공식 코드https://github.com/vidhishanair/structured_summarizer
공식 코드https://github.com/vietai/vit5
공식 코드ViT5: Pretrained Text-to-Text Transformer for Vietnamese Language Generation
https://github.com/vinairesearch/bartpho
공식 코드BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamese
https://github.com/violet-zct/fairseq-detect-hallucination
공식 코드Detecting Hallucinated Content in Conditional Neural Sequence Generation
https://github.com/wangclnlp/DeepSpeed-Chat-Extension/tree/main/examples/esrl
공식 코드ESRL: Efficient Sampling-based Reinforcement Learning for Sequence Generation
https://github.com/wbbeyourself/scm4llms
공식 코드Enhancing Large Language Model with Self-Controlled Memory Framework
https://github.com/webis-de/WWW-20
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.