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Abstractive Text Summarization
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Abstractive Text Summarization is the task of generating a short and concise summary that captures the salient ideas of the source text. The generated summaries potentially contain new phrases and sentences that may not appear in the source text.
Source: Generative Adversarial Network for Abstractive Text Summarization
Image credit: Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/robustness-gym/summvis
공식 코드SummVis: Interactive Visual Analysis of Models, Data, and Evaluation for Text Summarization
https://github.com/ryanzhumich/AESLC
공식 코드This Email Could Save Your Life: Introducing the Task of Email Subject Line Generation
https://github.com/ryokamoi/qa-metrics-human-annotation
공식 코드Shortcomings of Question Answering Based Factuality Frameworks for Error Localization
https://github.com/saiprabhakar/summarization_dpo_salt
공식 코드https://github.com/saketdingliwal/Abstractive-Dialogue-Summarization
공식 코드https://github.com/salesforce/booksum
공식 코드BookSum: A Collection of Datasets for Long-form Narrative Summarization
https://github.com/salesforce/hydra-sum
공식 코드HydraSum: Disentangling Stylistic Features in Text Summarization using Multi-Decoder Models
https://github.com/saxenarohit/moviesum
공식 코드MovieSum: An Abstractive Summarization Dataset for Movie Screenplays
https://github.com/saxenarohit/select_summ
공식 코드Select and Summarize: Scene Saliency for Movie Script Summarization
https://github.com/sayarghoshroy/Summaformers
공식 코드https://github.com/seasonyao/learnfromhumanedit
공식 코드https://github.com/sebastianGehrmann/bottom-up-summary
공식 코드https://github.com/shaojintian/power_law_decay
공식 코드https://github.com/sheffieldnlp/AMR2Text-summ
공식 코드https://github.com/shengqiang-zhang/plate
공식 코드Attention Temperature Matters in Abstractive Summarization Distillation
https://github.com/shichaosun/conabssum
공식 코드Alleviating Exposure Bias via Contrastive Learning for Abstractive Text Summarization
https://github.com/smartmediaai/personalsum
공식 코드PersonalSum: A User-Subjective Guided Personalized Summarization Dataset for Large Language Models
https://github.com/sobamchan/aclsum
공식 코드ACLSum: A New Dataset for Aspect-based Summarization of Scientific Publications
https://github.com/som-shahlab/clinfo.ai
공식 코드https://github.com/sosuperic/MeanSum
공식 코드MeanSum: A Neural Model for Unsupervised Multi-document Abstractive Summarization
https://github.com/ssm123ssm/docgpt-pharm
공식 코드https://github.com/stevenlau6/findsum
공식 코드https://github.com/suhara/sourcesum
공식 코드https://github.com/swarnahub/summarizationprograms
공식 코드Summarization Programs: Interpretable Abstractive Summarization with Neural Modular Trees
https://github.com/tafseer-nayeem/BengaliSummarization
공식 코드Unsupervised Abstractive Summarization of Bengali Text Documents
https://github.com/tagoyal/factuality-datasets
공식 코드Annotating and Modeling Fine-grained Factuality in Summarization
https://github.com/tahmedge/WSL-DS-COLING-2020
공식 코드https://github.com/tahmedge/preqfas
공식 코드Domain Adaptation with Pre-trained Transformers for Query Focused Abstractive Text Summarization
https://github.com/takase/control-length
공식 코드https://github.com/tanay2001/efactsum
공식 코드Improving Factuality of Abstractive Summarization without Sacrificing Summary Quality
https://github.com/tanyuqian/aspect-based-summarization
공식 코드Summarizing Text on Any Aspects: A Knowledge-Informed Weakly-Supervised Approach
https://github.com/temu-bsc/seq-to-seq-catalan
공식 코드https://github.com/tencent-ailab/grndpodcastsum
공식 코드Towards Abstractive Grounded Summarization of Podcast Transcripts
https://github.com/tencent-ailab/season
공식 코드Salience Allocation as Guidance for Abstractive Summarization
https://github.com/tensorflow/tensor2tensor
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.