https://github.com/TheAiSingularity/graphrag-local-ollama
From Local to Global: A Graph RAG Approach to Query-Focused Summarization
Papers with Code 아카이브
Summarization
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 2
- 평가표
- 14
- 보존된 평가 기록
- 2
Summarization is the task of producing a shorter version of one or several documents that preserves most of the input's meaning.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/bnitsan/papertweet
Bringing Structure into Summaries: a Faceted Summarization Dataset for Long Scientific Documents
https://github.com/gusye1234/nano-graphrag
From Local to Global: A Graph RAG Approach to Query-Focused Summarization
https://github.com/kashgupta/textsummarization
Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond
https://github.com/meghu2791/DeepLearningModels
Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond
https://github.com/microsoft/graphrag
From Local to Global: A Graph RAG Approach to Query-Focused Summarization
https://github.com/mirandrom/HipoRank
Discourse-Aware Unsupervised Summarization of Long Scientific Documents
https://github.com/seongminp/hdseg
Unsupervised Extractive Dialogue Summarization in Hyperdimensional Space
https://github.com/srpkdyy/CLIP-It
https://github.com/theamrzaki/text_summurization_abstractive_methods
Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond
https://github.com/tongbao96/code-for-sfr-as
A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents
https://github.com/yunzhusong/AAAI20-PORLHG
Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.