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/nlpyang/geval
공식 코드G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment
https://github.com/nlpyang/hiersumm
공식 코드https://github.com/nttcslab-sp-admin/tedsummary
공식 코드Attention-based Multi-hypothesis Fusion for Speech Summarization
https://github.com/ntunlp/chatgpt_eval
공식 코드A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark Datasets
https://github.com/ntunlp/middlesum
공식 코드On Context Utilization in Summarization with Large Language Models
https://github.com/ntunlp/summafusion
공식 코드https://github.com/ntunlp/summareranker
공식 코드SummaReranker: A Multi-Task Mixture-of-Experts Re-ranking Framework for Abstractive Summarization
https://github.com/ntunlp/summscore
공식 코드https://github.com/ntunlp/summscore-acl-findings-2023
공식 코드https://github.com/ntunlplab/fecs
공식 코드https://github.com/nusnlp/prs
공식 코드Preference-Guided Reflective Sampling for Aligning Language Models
https://github.com/nusnlp/reacts
공식 코드Just What You Desire: Constrained Timeline Summarization with Self-Reflection for Enhanced Relevance
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/odabashi/LMs-in-News-Summarization
공식 코드Unraveling the Capabilities of Language Models in News Summarization
https://github.com/odelliab/HowSumm
공식 코드HowSumm: A Multi-Document Summarization Dataset Derived from WikiHow Articles
https://github.com/ofa-sys/ofa
공식 코드https://github.com/omossad/fat-albert
공식 코드FAT ALBERT: Finding Answers in Large Texts using Semantic Similarity Attention Layer based on BERT
https://github.com/opengvlab/llmprune-besa
공식 코드BESA: Pruning Large Language Models with Blockwise Parameter-Efficient Sparsity Allocation
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/pgiouroukis/dual
공식 코드DUAL: Diversity and Uncertainty Active Learning for Text Summarization
https://github.com/phhartl/lrec_2022
공식 코드Applying Automatic Text Summarization for Fake News Detection
https://github.com/pittisl/greentrainer
공식 코드Towards Green AI in Fine-tuning Large Language Models via Adaptive Backpropagation
https://github.com/pku-ml/longppl
공식 코드What is Wrong with Perplexity for Long-context Language Modeling?
https://github.com/plm3332/fizz
공식 코드FIZZ: Factual Inconsistency Detection by Zoom-in Summary and Zoom-out Document
https://github.com/pltrdy/autoalign
공식 코드Align then Summarize: Automatic Alignment Methods for Summarization Corpus Creation
https://github.com/pluslabnlp/amrfact
공식 코드AMRFact: Enhancing Summarization Factuality Evaluation with AMR-Driven Negative Samples Generation
https://github.com/pluslabnlp/cdm
공식 코드Open-Domain Text Evaluation via Contrastive Distribution Methods
https://github.com/potsawee/longsum0
공식 코드Long-Span Summarization via Local Attention and Content Selection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.