https://github.com/rahular/varta
공식 코드Vārta: A Large-Scale Headline-Generation Dataset for Indic Languages
Papers with Code 아카이브
News Generation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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Generation of larger segments of text with consistent topic and evolving story.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/ryanzhumich/AESLC
공식 코드This Email Could Save Your Life: Introducing the Task of Email Subject Line Generation
https://github.com/scoopmatic/finnish-hockey-news-generation-paper
공식 코드Template-free Data-to-Text Generation of Finnish Sports News
https://github.com/shin285/KOMORAN
공식 코드Don't Just Scratch the Surface: Enhancing Word Representations for Korean with Hanja
https://github.com/sinhala-nlp/nsina
공식 코드https://github.com/sinhala-nlp/sinhala-headline-generation
공식 코드https://github.com/sinhala-nlp/sinhala-news-category-prediction
공식 코드https://github.com/sinhala-nlp/sinhala-news-media-identification
공식 코드https://github.com/sufenlp/stylebart
공식 코드https://github.com/timoschick/pet
공식 코드https://github.com/yaserkl/RLSeq2Seq
공식 코드https://github.com/yunzhusong/AAAI20-PORLHG
공식 코드Attractive or Faithful? Popularity-Reinforced Learning for Inspired Headline Generation
https://github.com/zhongping-zhang/engine
공식 코드Show, Write, and Retrieve: Entity-aware Article Generation and Retrieval
https://github.com/MorenoLaQuatra/bart-it
IT5: Text-to-text Pretraining for Italian Language Understanding and Generation
https://github.com/MrFeelgoood/RealEstateStocksForecasting
IT5: Text-to-text Pretraining for Italian Language Understanding and Generation
https://github.com/galsang/galsang
Don't Just Scratch the Surface: Enhancing Word Representations for Korean with Hanja
https://github.com/idc-dsi/Arapet
https://github.com/previtus/fake_news_generation_mark_I
https://github.com/sebajoe/thresh
Thresh: A Unified, Customizable and Deployable Platform for Fine-Grained Text Evaluation
https://github.com/theamrzaki/text_summurization_abstractive_methods
https://github.com/youngerous/qtree
LongForm: Effective Instruction Tuning with Reverse Instructions
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.