自然语言处理

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文本摘要

浏览 162 扫码 分享 2022-07-22 21:04:30
  • Lead算法和Rouge评估实现
  • Match_Sum复现
  • Search strategy
  • BPE Subword
  • 结营报告

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  • 学术小记
    • 2022.05
      • Wat zei je? Detecting Out-of-Distribution Translations with Variational Transformers
      • BRIO: Bringing Order to Abstractive Summarization
      • SimCLS: A Simple Framework for Contrastive Learning of Abstractive Summarization
    • 2022.03
      • 22.03.08 Improving Evidence Retrieval for Automated Explainable Fact-Checking
      • 22.03.07 Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels
      • 22.03.05 Controllable Natural Language Generation with Contrastive Prefixes
      • 22.03.05 Text Smoothing: Enhance Various Data Augmentation Methods on Text Classification Tasks
      • 22.03.05 Exploring the Impact of Negative Samples of Contrastive Learning: A Case Study of Sentence Embedding
      • 22.03.04 SpanBert
      • 22.03.02 Improving Language Models by Retrieving from Trillions of Tokens
      • 22.03.02 Retrieval Augmented NLP
      • 22.03.01 Knowledgeable Prompt-tuning: Incorporating Knowledge into Prompt Verbalizer for Text Classification
    • 2022.02
      • 22.02.28 Bert-Whitening
      • 22.02.27 各向异性
      • 22.02.27 Flooding-X: Improving BERT’s Resistance to Adversarial Attacks via Loss-Restricted Fine-Tuning
      • 22.02.26 Impact of Pretraining Term Frequencies on Few-Shot Reasoning
      • 22.02.19 A Primer in BERTology: What We Know About How BERT Works
      • 22.02.19 模型压缩 From Dense to Sparse: Contrastive Pruning for Better Pre-trained Language Model Compression
    • 2022.01
      • 21.01.28 苏神博客
      • 21.01.23 重读DQN
      • 21.01.22 DATASET DISTILLATION
      • 21.01.22 Adversarial NLI: A New Benchmark for Natural Language Understanding【ACL20】
      • 21.01.21 重读变分推断
      • 21.01.18 ExtraPhrase: Efficient Data Augmentation for Abstractive Summarization
    • 2021.12
      • 21.12.30 Robust Neural Machine Translation with Doubly Adversarial Inputs
      • 21.12.30 Towards a Universal Continuous Knowledge Base 连续性知识库
      • 21.12.20 Asking and Answering Questions to Evaluate the Factual Consistency of Summaries
      • 21.12.20 Knowledge Graph-Augmented Abstractive Summarization with Semantic-Driven Cloze Reward
      • 21.12.20 FEQA: A Question Answering Evaluation Framework for Faithfulness Assessment in Abstractive Summarization
      • 21.12.20 Ranking Generated Summaries by Correctness: An Interesting but Challenging Application for Natural Language Inference
      • 21.12.20 Ensure the Correctness of the Summary: Incorporate Entailment Knowledge into Abstractive Sentence Summarization
      • 21.12.20 Mind The Facts: Knowledge-Boosted Coherent Abstractive Text Summarization [NIPS19]
      • 21.12.19 Improving Truthfulness of Headline Generation
      • 21.12.18 Multi-Fact Correction in Abstractive Text Summarization.
      • 21.12.18 Reducing Quantity Hallucinations in Abstractive Summarization【EMNLP20】
      • 21.12.18 GO FIGURE: A Meta Evaluation of Factuality in Summarization
      • 21.12.18 Truth or Error? Towards systematic analysis of factual errors in abstractive summaries
      • 21.12.17 Constrained Abstractive Summarization: Preserving Factual Consistency with Constrained Generation
      • 21.12.17 Detecting Hallucinated Content in Conditional Neural Sequence Generation
      • 21.12.16 Assessing The Factual Accuracy of Generated Text
      • 21.12.15 Enhancing Factual Consistency of Abstractive Summarization
      • 21.12.15 QuestEval: Summarization Asks for Fact-based Evaluation
      • 21.12.11 Improving Faithfulness in Abstractive Summarization with Contrast Candidate Generation and Selection 【NAACL2021】
      • 21.12.9 Incorporating Commonsense Knowledge into Abstractive Dialogue Summarization via Heterogeneous Graph Networks
      • 21.12.2 Understanding Factuality in Abstractive Summarization with FRANK: A Benchmark for Factuality Metrics
      • 21.12.2 Annotating and Modeling Fine-grained Factuality in Summarization
      • 21.12.1 Inspecting the Factuality of Hallucinated Entities in Abstractive Summarization
      • 21.12.1 Investigating Crowdsourcing Protocols for Evaluating the Factual Consistency of Summaries
    • 2021.11
      • 21.11.30 MOFE: MIXTURE OF FACTUAL EXPERTS FOR CONTROLLING HALLUCINATIONS IN ABSTRACTIVE SUMMARIZATION
      • 21.11.30 Fine-grained Factual Consistency Assessment for Abstractive Summarization Models 【EMNLP21】
      • 21.11.30 Dialogue Inspectional Summarization with Factual Inconsistency Awareness
      • 21.11.30 SUMMAC: Re-Visiting NLI-based Models for Inconsistency Detection in Summarization 【TACL】
      • 21.11.29 Are Factuality Checkers Reliable? Adversarial Meta-evaluation of Factuality in Summarization [EMNLP21]
      • 21.11.28
      • 21.11.27 Factual Probing Is [MASK]: Learning vs. Learning to Recall【NAACL2021】
      • 21.11.27 BARTSCORE: Evaluating Generated Text as Text Generation 【NIPS2021】
      • 21.11.26 Prefix-Tuning: Optimizing Continuous Prompts for Generation [ACL2021]
      • 21.11.24 相似度分析
      • 21.11.22 DEEP: DEnoising Entity Pre-training for Neural Machine Translation
      • 21.11.22 NJU HPC Tips
      • 21.11.22 NLP多进程处理框架 如何高效处理文本？
      • 21.11.19 BM25和TFIDF转载
      • 21.11.19 SJL博客6篇
      • 21.11.18 转载苏巨 Bert初始化以及Norm，梯度消失的讨论
      • 21.11.18 SJL博客8篇
      • 21.11.18 Discourse Understanding and Factual Consistency in Abstractive Summarization EACL2021
      • 21.11.17 SJL博客3篇
      • 21.11.16 Factual Error Correction for Abstractive Summarization Models EMNLP20 short
      • 21.11.16 NLP From Scratch Without Large-Scale Pretraining: A Simple and Efficient Framework
      • 21.11.15 初步做的论文Servey
      • 21.11.6 EMNLP2021 论文预讲笔记
      • 21.11.5 Fairseq Code review
      • 21.11.4 PEGASUS Pre-training with Extracted Gap-sentences for Abstractive Summarization【ICML 2020】
      • 21.11.3 On Faithfulness and Factuality in Abstractive Summarization【ACL 2020】
      • 21.11.1 BERTSCORE: EVALUATING TEXT GENERATION WITH BERT 【ICLR2020】
      • 21.11.1 Focus Attention: Promoting Faithfulness and Diversity in Summarization【ACL2021】 pending
      • 21.11.1 采样策略
    • 2021.10
      • 21.10.31 Factual Consistency Evaluation for Text Summarization via Counterfactual Estimation 【EMNLP2021】
      • 21.10.29 Faithful or Extractive? On Mitigating the Faithfulness-Abstractiveness Trade-off in Abstractive Summarization
      • 21.10.27 MiRANews: Dataset and Benchmarks for Multi-Resource-Assisted News Summarization 【EMNLP2021】
      • 21.10.26 Improving Factual Consistency of Abstractive Summarization via Question Answering 【pending】
      • 21.10.25 Language Model as an Annotator: Exploring DialoGPT for Dialogue Summarization
      • 21.10.24 CTRLSUM: TOWARDS GENERIC CONTROLLABLE TEXT SUMMARIZATION
      • 21.10.20 Training Dynamics for Text Summarization Models
      • 21.10.15 GSum: A General Framework for Guided Neural Abstractive Summarization 【NAACL 2021】
      • 21.10.10 VAETransformer进一步实验
      • 21.10.3 CLIFF: Contrastive Learning for Improving Faithfulness and Factuality in Abstractive Summarization 【EMNLP2021】
      • 21.10.1 Controllable Neural Dialogue Summarization with Personal Named Entity Planning 【EMNLP2021】
    • 2021.9
      • 21.9.29 VAE Transformer
      • 21.9.26 Enriching and Controlling Global Semantics for Text Summarization
      • 21.9.25 VAE变分编码器
      • 21.9.24 GAN原理和实践
      • 21.9.22 A Bag of Tricks for Dialogue Summarization【EMNLP2021】
      • 21.9.16 相似度匹配若干新算法
      • 21.9.16 R-Drop
      • 21.9.14 在BERT里Seq2Seq
      • 21.9.13 prompt方法
    • Before
      • GNN in NLP
      • NJU HPC 高性能计算Guide
      • Fairseq 源码分析
      • 对抗学习 in NLP
      • Trick in DL NLP
      • 对比学习
      • 自监督学习Self-Supervise Learning
      • CCL 2020 Note
      • Seq2Seq Translation
    • 奇怪的技巧
  • 懒得整理
    • 方向实践
      • 关系抽取 Relation Extraction
        • Bert in RE
        • Relation Extraction
      • Q&A
        • QA资料
      • 文本摘要
        • Lead算法和Rouge评估实现
        • Match_Sum复现
        • Search strategy
        • BPE Subword
        • 结营报告
      • NER
        • 初试实体识别
        • ChineseBertNer
        • ChineseBertNER2
        • 新数据的BertNer
      • TextMatching
        • ESIM
        • Text_Matching 比赛
        • Text_Matching调参优化
        • Text_Matching总结
    • 其他
      • Neo4j
        • neo4j图形数据库入门
      • Attention
      • 建立Wiki词典
      • 导入Wiki词典
      • 阅读信息
    • NLP's Model
      • Word2Vec
        • Word2vec's Skip_Gram模型
        • Skip_Gram中文应用
        • Skip_Gram模型（补）
        • Skip_Gram中文实用
      • Bert
        • Classification Bert
        • Bert for Ner 代码分析
        • ChineseBertNer
        • 配置Bert
        • Bert初试
        • Bert魔改
        • 服务器部署环境
        • Bert 再战Kaggle（句子分类）
        • Bert pretrain
      • LSTM
        • LSTM 入门（词性标注）
        • LSTM Kaggle 实战（句子分类）
        • LSTM 再战Kaggle（句子分类)
    • NLP's API
      • NLTK入门
      • jieba入门
      • hanlp
  • 以前的论文笔记
    • 事实一致性
      • The Factual Inconsistency Problem in Abstractive Text Summarization A Survey
    • 对话摘要 Dialog Summarization
      • Improving Abstractive Dialogue Summarization with Graph Structures
      • ABSTRACTIVE DIALOG SUMMARIZATION WITH SEMANTIC SCAFFOLDS
    • Two-stage encoding Extractive Summarization
    • Neural Document Summarization by Jointly Learning to Score and Select Sentences
    • CycleGT: Unsupervised Graph-to-Text and Text-to-Graph Generation via Cycle Training
    • CoLAKE: Contextualized Language and Knowledge Embedding
    • A Closer Look at Data Bias in Neural Extractive Summarization Models
    • Deep Communicating Agents for Abstractive Summarization
    • Abstractive News Summarization based on Event Semantic Link Network
    • A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents
    • BART Denoising
    • What Have We Achieved on Text Summarization?
    • Abstractive Summarization: A Survey of the State of the Art
    • Summary Level Training of Sentence Rewriting for Abstractive Summarization
    • Heterogeneous Graph Neural Networks
    • STRUCTURED NEURAL SUMMARIZATION
    • Text Summarization with Pretrained Encoders
    • SummaRuNNer
    • Attention is All you Need
    • Toward Making the Most of Context in NMT
    • Acquiring Knowledge from Pre-trained Model to Neural Machine Translation
    • Text Summarization Techniques
    • Extractive Summarization as Text Matching
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