https://github.com/alibaba/AliceMind/tree/main/PALM
공식 코드PALM: Pre-training an Autoencoding&Autoregressive Language Model for Context-conditioned Generation
Papers with Code 아카이브
Abstractive Text Summarization
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Abstractive Text Summarization is the task of generating a short and concise summary that captures the salient ideas of the source text. The generated summaries potentially contain new phrases and sentences that may not appear in the source text.
Source: Generative Adversarial Network for Abstractive Text Summarization
Image credit: Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/alirezasalemi7/arman
공식 코드https://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-science/SigExt
공식 코드Salient Information Prompting to Steer Content in Prompt-based Abstractive Summarization
https://github.com/amazon-science/ceret-llm-refine
공식 코드CERET: Cost-Effective Extrinsic Refinement for Text Generation
https://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/anshuman23/llm_position_bias
공식 코드https://github.com/anum94/adapteval
공식 코드AdaptEval: Evaluating Large Language Models on Domain Adaptation for Text Summarization
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/axenov/BERT-Summ-OpenNMT
공식 코드Abstractive Text Summarization based on Language Model Conditioning and Locality Modeling
https://github.com/baoguangsheng/gemini
공식 코드GEMINI: Controlling the Sentence-level Writing Style for Abstractive Text Summarization
https://github.com/bionlproc/text-summ-for-doc-retrieval
공식 코드Literature Retrieval for Precision Medicine with Neural Matching and Faceted Summarization
https://github.com/bloomberg/kbir_keybart
공식 코드https://github.com/botondbarta/hunsum
공식 코드From News to Summaries: Building a Hungarian Corpus for Extractive and Abstractive Summarization
https://github.com/cannylab/summary_loop
공식 코드The Summary Loop: Learning to Write Abstractive Summaries Without Examples
https://github.com/casszhao/prunehall
공식 코드Investigating Hallucinations in Pruned Large Language Models for Abstractive Summarization
https://github.com/cestwc/universal-evasion
공식 코드https://github.com/chatc/summ-n
공식 코드Summ^N: A Multi-Stage Summarization Framework for Long Input Dialogues and Documents
https://github.com/chenlong-clock/DTELS-Bench
공식 코드DTELS: Towards Dynamic Granularity of Timeline Summarization
https://github.com/chenxinan-fdu/colo
공식 코드COLO: A Contrastive Learning based Re-ranking Framework for One-Stage Summarization
https://github.com/chicagohai/entity-based-political-bias
공식 코드Entity-Based Evaluation of Political Bias in Automatic Summarization
https://github.com/chz816/esacl
공식 코드Enhanced Seq2Seq Autoencoder via Contrastive Learning for Abstractive Text Summarization
https://github.com/chz816/podcast-summarization-baseline
공식 코드https://github.com/chz816/tas
공식 코드Topic-Guided Abstractive Text Summarization: a Joint Learning Approach
https://github.com/cimeister/typical-sampling
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.