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Text Summarization
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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/chz816/tas
공식 코드Topic-Guided Abstractive Text Summarization: a Joint Learning Approach
https://github.com/cimeister/typical-sampling
공식 코드https://github.com/cjaep/MPO
공식 코드Model-based Preference Optimization in Abstractive Summarization without Human Feedback
https://github.com/ckbjimmy/clneg
공식 코드Clinical Text Summarization with Syntax-Based Negation and Semantic Concept Identification
https://github.com/cliveyn/pdsum
공식 코드PDSum: Prototype-driven Continuous Summarization of Evolving Multi-document Sets Stream
https://github.com/clovaai/FocusSeq2Seq
공식 코드https://github.com/cmhcbb/Seq2Sick
공식 코드Seq2Sick: Evaluating the Robustness of Sequence-to-Sequence Models with Adversarial Examples
https://github.com/cmsflash/efficient-attention
공식 코드https://github.com/coldmist-lu/erroranalysis_prompt
공식 코드Error Analysis Prompting Enables Human-Like Translation Evaluation in Large Language Models
https://github.com/complementizer/rl-sentence-compression
공식 코드Efficient Unsupervised Sentence Compression by Fine-tuning Transformers with Reinforcement Learning
https://github.com/complementizer/wcep-mds-dataset
공식 코드A Large-Scale Multi-Document Summarization Dataset from the Wikipedia Current Events Portal
https://github.com/computationalmedia/supmmd
공식 코드SupMMD: A Sentence Importance Model for Extractive Summarization using Maximum Mean Discrepancy
https://github.com/congboma/disentanglesum
공식 코드Disentangling Specificity for Abstractive Multi-document Summarization
https://github.com/congressRA/sample-agent
공식 코드Agent-Enhanced Large Language Models for Researching Political Institutions
https://github.com/craffel/mad
공식 코드Online and Linear-Time Attention by Enforcing Monotonic Alignments
https://github.com/craffel/mocha
공식 코드https://github.com/crisisltlsum/crisistimelines
공식 코드CrisisLTLSum: A Benchmark for Local Crisis Event Timeline Extraction and Summarization
https://github.com/csebuetnlp/crosssum
공식 코드CrossSum: Beyond English-Centric Cross-Lingual Summarization for 1,500+ Language Pairs
https://github.com/csebuetnlp/xl-sum
공식 코드XL-Sum: Large-Scale Multilingual Abstractive Summarization for 44 Languages
https://github.com/csyanghan/alignsum
공식 코드AlignSum: Data Pyramid Hierarchical Fine-tuning for Aligning with Human Summarization Preference
https://github.com/cylnlp/DialogSum
공식 코드DialogSum: A Real-Life Scenario Dialogue Summarization Dataset
https://github.com/damo-nlp-sg/hierencdec
공식 코드A Hierarchical Encoding-Decoding Scheme for Abstractive Multi-document Summarization
https://github.com/damo-nlp-sg/llm_summeval
공식 코드Large Language Models are Not Yet Human-Level Evaluators for Abstractive Summarization
https://github.com/danielabweiss/extending-sentence-fusion-resources
공식 코드Extending Multi-Text Sentence Fusion Resources via Pyramid Annotations
https://github.com/danielabweiss/qa-align
공식 코드QA-Align: Representing Cross-Text Content Overlap by Aligning Question-Answer Propositions
https://github.com/darsh10/Nutribullets
공식 코드Nutri-bullets: Summarizing Health Studies by Composing Segments
https://github.com/data-lang/text-summarization-sota-experiment
공식 코드Survey on Abstractive Text Summarization: Dataset, Models, and Metrics
https://github.com/dcdmllm/ideal_summary
공식 코드https://github.com/declare-lab/wikides
공식 코드WikiDes: A Wikipedia-Based Dataset for Generating Short Descriptions from Paragraphs
https://github.com/deepspeedai/deepspeed
공식 코드Arctic Long Sequence Training: Scalable And Efficient Training For Multi-Million Token Sequences
https://github.com/dengyang17/msg
공식 코드https://github.com/dengyang17/wikihowQA
공식 코드Joint Learning of Answer Selection and Answer Summary Generation in Community Question Answering
https://github.com/dennlinger/klexikon
공식 코드Klexikon: A German Dataset for Joint Summarization and Simplification
https://github.com/dennlinger/summaries
공식 코드https://github.com/devichand579/HPT
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.