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/akabircs/belin
공식 코드BeliN: A Novel Corpus for Bengali Religious News Headline Generation using Contextual Feature Fusion
https://github.com/alebryvas/berk266
공식 코드Abstractive Summarization of Spoken andWritten Instructions with BERT
https://github.com/alexrame/rewardedsoups
공식 코드https://github.com/alibaba/AliceMind/tree/main/PALM
공식 코드PALM: Pre-training an Autoencoding&Autoregressive Language Model for Context-conditioned Generation
https://github.com/alipay/PainlessInferenceAcceleration
공식 코드https://github.com/alirezasalemi7/arman
공식 코드https://github.com/allenai/ms2
공식 코드https://github.com/allenai/mslr-annotated-dataset
공식 코드Automated Metrics for Medical Multi-Document Summarization Disagree with Human Evaluations
https://github.com/allenai/pinocchio
공식 코드https://github.com/allenai/primer
공식 코드PRIMERA: Pyramid-based Masked Sentence Pre-training for Multi-document Summarization
https://github.com/allenai/scitldr
공식 코드https://github.com/amankhullar/mast
공식 코드MAST: Multimodal Abstractive Summarization with Trimodal Hierarchical Attention
https://github.com/amanuelf/biomed_abstractive_summarization
공식 코드Entity-driven Fact-aware Abstractive Summarization of Biomedical Literature
https://github.com/amazon-research/adasum
공식 코드https://github.com/amazon-research/fact-check-summarization
공식 코드Entity-level Factual Consistency of Abstractive Text Summarization
https://github.com/amazon-research/fact-graph
공식 코드FactGraph: Evaluating Factuality in Summarization with Semantic Graph Representations
https://github.com/amazon-research/meta-learning-the-difference
공식 코드Meta-Learning the Difference: Preparing Large Language Models for Efficient Adaptation
https://github.com/amazon-research/summary-reference-revision
공식 코드https://github.com/amazon-science/SigExt
공식 코드Salient Information Prompting to Steer Content in Prompt-based Abstractive Summarization
https://github.com/amazon-science/SumREN
공식 코드https://github.com/amazon-science/ceret-llm-refine
공식 코드CERET: Cost-Effective Extrinsic Refinement for Text Generation
https://github.com/amazon-science/controllable-readability-summarization
공식 코드https://github.com/amazon-science/crispo
공식 코드CriSPO: Multi-Aspect Critique-Suggestion-guided Automatic Prompt Optimization for Text Generation
https://github.com/amazon-science/faithful-summarization-generation
공식 코드Faithfulness-Aware Decoding Strategies for Abstractive Summarization
https://github.com/amazon-science/summarization-sicf-score
공식 코드Semi-Supervised Dialogue Abstractive Summarization via High-Quality Pseudolabel Selection
https://github.com/amazon-science/tofueval
공식 코드TofuEval: Evaluating Hallucinations of LLMs on Topic-Focused Dialogue Summarization
https://github.com/anshuman23/llm_position_bias
공식 코드https://github.com/antoniosudoso/attention-nilm
공식 코드Improving Non-Intrusive Load Disaggregation through an Attention-Based Deep Neural Network
https://github.com/anum94/adapteval
공식 코드AdaptEval: Evaluating Large Language Models on Domain Adaptation for Text Summarization
https://github.com/applicaai/pyramidions
공식 코드Sparsifying Transformer Models with Trainable Representation Pooling
https://github.com/ariecattan/multi_mds
공식 코드https://github.com/arminazizi98/lamda
공식 코드LaMDA: Large Model Fine-Tuning via Spectrally Decomposed Low-Dimensional Adaptation
https://github.com/artidoro/frank
공식 코드Understanding Factuality in Abstractive Summarization with FRANK: A Benchmark for Factuality Metrics
https://github.com/arvind-balaji/debate-cards
공식 코드DebateSum: A large-scale argument mining and summarization dataset
https://github.com/ashokurlana/Indian-Language-Summarization
공식 코드Indian Language Summarization using Pretrained Sequence-to-Sequence Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.