文本摘要的一些研究概念

主要翻译自github，特别适合新手查看。我也是新手，看下面的翻译就知道了。

生成方式（Generation Way）

gen-ext：提取式摘要？第一个就遇到了困难。
gen-abs：抽象式摘要？
gen-2stage：两个混合，压缩和混合

回归方式（Regressive Way）

regr-auto: Autoregressive Decoder (Pointer network) 自回归解码器，指针网络
regr-nonauto: Non-autoregressive Decoder (Sequence labeling) 非自回归解码器，序列标签

任务设定（Task Settings）

task-singleDoc: Single-document Summarization 单文本摘要
task-multiDoc: Multi-document Summarization 多文本摘要
task-senCompre: Sentence Compression 句子压缩
task-sci: Scientific Paper 科技论文
task-radiologyReport: Radiology Reports 放射科报告？？这玩意怎么跑到这的？
task-multimodal: Multi-modal Summarization 多模型摘要/多模型汇总
task-aspect: Aspect-based Summarization 基于方面的摘要？？？
task-opinion: Opinion Summarization 可选择摘要
task-review: Review Summarization 摘要综述？？？
task-meeting: Meeting-based Summarization 基于会议的摘要
task-conversation: Consersation-based Summarization 基于会话的摘要
task-medical: Medical text-related Summarization 关于医学文本摘要
task-covid: COVID-19 related Summarization 关于新冠病毒的摘要
task-query: query-based Summarization 基于查询的摘要
task-question: question-based Summarization 基于问答的摘要
task-video: Video-based Summarization 基于视频的摘要
task-code: Source Code Summarization 源码摘要
task-control: Controllable Summarization 可控制的摘要
task-event: Event-based Summarization 基于事件的摘要
task-longtext: Summarization for Long Text 长文本摘要
task-knowledge: Text Summarization with External Knowledge 可提取知识文本摘要
task-highlight: Pick out important content and emphasize 选出重要内容并强调
task-analysis: Model Understanding or Interpretability 模型的可理解性或可解释性
task-novel: Novel Chapter Generation 新章节的产生，novel在这里做形容词吧
task-argument: Automatic Argument Summarization 自动参数摘要

架构-Architecture (Mechanism)

arch-rnn: Recurrent Neural Networks (LSTM, GRU) 递归神经网络
arch-cnn: Convolutional Neural Networks (CNN) 循环神经网络
arch-transformer: Transformer 翻译器
arch-graph: Graph Neural Networks or Statistic Graph Models 图神经网络或者统计图模型
arch-gnn: Graph Neural Networks 图神经网络
arch-textrank: TextRank 不翻译
arch-att: Attention Mechanism 注意力机制
arch-pointer: Pointer Layer 在这里应该不是指针层，不是输入层，不是输出层，肯定就是隐藏层了。
arch-coverage: Coverage Mechanism 覆盖机制？？？

训练（Training）

train-sup: Supervised Learning 监督学习
train-unsup: Unsupervised Learning 非监督学习
train-weak: (implies train-sup): Weakly Supervised Learning 弱监督学习
train-multitask: Multi-task Learning 多任务学习
train-multilingual: Multi-lingual Learning 多语言学习
train-multimodal: Multi-modal Learning 多模型学习
train-auxiliary: Joint Training 连接学习
train-transfer: Cross-domain Learning, Transfer Learning, Domain Adaptation 跨领域学习，转移学习，领域适应
train-active: Active Learning, Boostrapping 主动学习，助人为乐？什么翻译
train-adver: Adversarial Learning 对抗学习
train-template: Template-based Summarization 基于模板的摘要
train-augment: Data Augmentation 数据参数
train-curriculum: Curriculum Learning 课程学习？
train-lowresource: Low-resource Summarization 低资源摘要
train-retrieval: Retrieval-based Summarization 基于检索的摘要
train-meta: Meta-learning 元学习

预训练模型（Pre-trained Models）

pre-word2vec: word2vec
pre-glove: GLoVe
pre-bert: BERT
pre-elmo: ELMo
pre-hibert: HiBERT
pre-bart: BART
pre-pegasus: PEGASUS
pre-unilm: UNILM
pre-mass: MASS
pre-T5: Text-to-Text Transfer Transformer
pre-S2ORC: Pretrained model on semantic scholar open research corpus
pre-sciBERT: Scientific paper based pre-trained model
pre-SPECTER: Scientific Paper Embeddings using Citationinformed TransformERs

不可微函数的松弛/训练方法（Relaxation/Training Methods for Non-differentiable Functions）

这里应该是针对不可导不可微的一些处理方法。softmax曾经看到过。

nondif-straightthrough: Straight-through Estimator
nondif-gumbelsoftmax: Gumbel Softmax
nondif-minrisk: Minimum Risk Training
nondif-reinforce: REINFORCE

对抗方法（Adversarial Methods）

adv-gan: Generative Adversarial Networks 生成对抗网络
adv-feat: Adversarial Feature Learning 对抗特征学习
adv-examp: Adversarial Examples 对抗样例
adv-train: Adversarial Training 对抗训练

潜在变量模型（Latent Variable Models）

latent-vae: Variational Auto-encoder 可变自动编码器
latent-topic: Topic Model

数据集（Dataset）

data-new: Constructing a new dataset 组件新的数据集
data-annotation: Annotation Methodology 注释方法

评价（Evaluation）

这里应该就是说你的实验出来结果，怎么评价你的文本摘要出来是符合标注还是不符合标注的，有机构有人去专门评价你的工作。

eval-human: Human Evaluation 人类评价
eval-metric-rouge: ROUGE 一个机构
eval-metric-bertscore: BERTScore
eval-aspect-coherence: Coherence
eval-aspect-redundancy: Redundancy of Summary
eval-aspect-factuality: Factuality
eval-aspect-abstractness: Abstractness
eval-referenceQuality: Reference Quality
eval-metric-learnable: Metrics are Learnable
eval-optimize-humanJudgement: Optimization towards human judgement
eval-reference-less: Reference-less Approach to Automatic Evaluation
eval-metric-unsupervised: Unsupervised Automatic Evaluation

Survey

survey-2020: A survey paper in 2020