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Text Summarization
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- 하위 태스크
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- 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/ghazaleh-mahmoodi/Prompting_LLMs_AS_Explainable_Metrics
공식 코드Exploring Prompting Large Language Models as Explainable Metrics
https://github.com/ghchen18/acl22-sixtp
공식 코드Towards Making the Most of Multilingual Pretraining for Zero-Shot Neural Machine Translation
https://github.com/giganttheo/importance-based-relevance-score
공식 코드https://github.com/gonced8/document-summarization
공식 코드https://github.com/google-research-datasets/ToTTo
공식 코드https://github.com/google-research-datasets/aquamuse
공식 코드AQuaMuSe: Automatically Generating Datasets for Query-Based Multi-Document Summarization
https://github.com/google-research-datasets/wikifact
공식 코드https://github.com/google-research-datasets/xsum_hallucination_annotations
공식 코드https://github.com/google-research/bigbird
공식 코드https://github.com/google-research/google-research
공식 코드SummAE: Zero-Shot Abstractive Text Summarization using Length-Agnostic Auto-Encoders
https://github.com/google-research/lasertagger
공식 코드https://github.com/google-research/longt5
공식 코드LongT5: Efficient Text-To-Text Transformer for Long Sequences
https://github.com/google-research/pegasus
공식 코드PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization
https://github.com/gospelnnadi/text-summarization-sota-experiment
공식 코드Survey on Abstractive Text Summarization: Dataset, Models, and Metrics
https://github.com/gsh199449/proto-summ
공식 코드https://github.com/guokan-shang/ami-and-icsi-corpora
공식 코드https://github.com/guokan-shang/elitr-minuting-corpus
공식 코드https://github.com/gurdaspuriya/cawa
공식 코드https://github.com/h2oai/h2o-llmstudio
공식 코드https://github.com/hannibal046/selfmemory
공식 코드Lift Yourself Up: Retrieval-augmented Text Generation with Self Memory
https://github.com/hanyin88/llama-clinic
공식 코드Towards Adapting Open-Source Large Language Models for Expert-Level Clinical Note Generation
https://github.com/hddbang/PolyTope
공식 코드https://github.com/he159ok/benchmark-of-uncertainty-estimation-methods-in-text-summarization
공식 코드Can We Trust the Performance Evaluation of Uncertainty Estimation Methods in Text Summarization?
https://github.com/healthylaife/ConTextual
공식 코드https://github.com/helmertz/querysum
공식 코드https://github.com/helmertz/querysum-data
공식 코드https://github.com/hemingkx/gad
공식 코드Speculative Decoding: Exploiting Speculative Execution for Accelerating Seq2seq Generation
https://github.com/hemingkx/specdec
공식 코드Speculative Decoding: Exploiting Speculative Execution for Accelerating Seq2seq Generation
https://github.com/heyjing/srlscore
공식 코드Evaluating Factual Consistency of Texts with Semantic Role Labeling
https://github.com/hiyouga/llama-factory
공식 코드https://github.com/hkust-knowcomp/knowledge-constrained-decoding
공식 코드KCTS: Knowledge-Constrained Tree Search Decoding with Token-Level Hallucination Detection
https://github.com/hkust-knowcomp/livesum
공식 코드https://github.com/hkust-knowcomp/livesum-ttt
공식 코드https://github.com/hltchkust/vg-gplms
공식 코드Vision Guided Generative Pre-trained Language Models for Multimodal Abstractive Summarization
https://github.com/homealim2012/QE_Mani_Summary
공식 코드Using Query Expansion in Manifold Ranking for Query-Oriented Multi-Document Summarization
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.