Papers Abstractive Text Summarization
“Abstractive Text Summarization” 태그가 달린 논문 852편 · 필터 해제
ClueGraphSum: Let Key Clues Guide the Cross-Lingual Abstractive Summarization
Cross-Lingual Summarization (CLS) is the task to generate a summary in one language for an article in a different language. Previous studies on CLS mainly take pipeline methods or train the end-to-end model using the tra…
Abstractive Text SummarizationCross-Lingual Abstractive SummarizationDecoderSentenceControlling the Focus of Pretrained Language Generation Models
The finetuning of pretrained transformer-based language generation models are typically conducted in an end-to-end manner, where the model learns to attend to relevant parts of the input by itself. However, there does no…
Abstractive Text SummarizationResponse GenerationText GenerationLearning Cluster Patterns for Abstractive Summarization
Nowadays, pre-trained sequence-to-sequence models such as BERTSUM and BART have shown state-of-the-art results in abstractive summarization. In these models, during fine-tuning, the encoder transforms sentences to contex…
Abstractive Text SummarizationDecoderSequence-to-Sequence Resources for Catalan
In this work, we introduce sequence-to-sequence language resources for Catalan, a moderately under-resourced language, towards two tasks, namely: Summarization and Machine Translation (MT). We present two new abstractive…
Abstractive Text SummarizationMachine TranslationTranslationDifferentiable N-gram Objective on Abstractive Summarization
ROUGE is a standard automatic evaluation metric based on n-grams for sequence-to-sequence tasks, while cross-entropy loss is an essential objective of neural network language model that optimizes at a unigram level. We p…
Abstractive Text SummarizationLanguage ModelingLanguage ModellingSurvey of Hallucination in Natural Language Generation
Natural Language Generation (NLG) has improved exponentially in recent years thanks to the development of sequence-to-sequence deep learning technologies such as Transformer-based language models. This advancement has le…
Abstractive Text SummarizationData-to-Text GenerationDialogue GenerationGenerative Question Answering+5Locally Typical Sampling
Today's probabilistic language generators fall short when it comes to producing coherent and fluent text despite the fact that the underlying models perform well under standard metrics, e.g., perplexity. This discrepancy…
Abstractive Text SummarizationStory GenerationText GenerationSubmodularity In Machine Learning and Artificial Intelligence
In this manuscript, we offer a gentle review of submodularity and supermodularity and their properties. We offer a plethora of submodular definitions; a full description of a number of example submodular functions and th…
Abstractive Text SummarizationBIG-bench Machine Learningfeature selectionUnsupervised Multi-Granularity Summarization
Text summarization is a user-preference based task, i.e., for one document, users often have different priorities for summary. As a key aspect of customization in summarization, granularity is used to measure the semanti…
Abstractive Text SummarizationText SummarizationMeasuring Faithfulness of Abstractive Summaries
Recent abstractive summarization systems fail to generate factually consistent – faithful – summaries, which heavily limits their practical application. Commonly, these models tend to mix concepts from the source or hall…
Abstractive Text SummarizationIncorporating Question Answering-Based Signals into Abstractive Summarization via Salient Span Selection
In this work, we propose a method for incorporating question-answering (QA) signals into a summarization model. Our method identifies salient noun phrases (NPs) in the input document by automatically generating wh-questi…
Abstractive Text SummarizationQuestion AnsweringMosaic Augmentation for Text: Cropping and Collaging as Cross-Domain Techniques
We present new visually inspired cropping and collaging data augmentations for text. We test how these augmentations impact data-scarce scenarios over multiple NLP tasks: name entity recognition, extractive question answ…
Abstractive Text SummarizationExtractive Question-AnsweringQuestion AnsweringTopic-controllable Abstractive Summarization
Existing approaches for topic-controllable summarization either incorporate topic embeddings or modify the attention mechanism. The incorporation of such approaches in a particular summarization model requires the adapta…
Abstractive Text SummarizationBOOKSUM: A Collection of Datasets for Long-form Narrative Summarization
The majority of existing text summarization datasets include short-form source documents that lack long-range causal and temporal dependencies, and often contain strong layout and stylistic biases. While relevant, such …
Abstractive Text SummarizationFormLong-Form Narrative SummarizationText SummarizationFalsesum: Generating Document-level NLI Examples for Recognizing Factual Inconsistency in Summarization
Neural abstractive summarization models are prone to generate summaries that are factually inconsistent with their source documents. Previous work has introduced the task of recognizing such factual inconsistency as a do…
Abstractive Text SummarizationNatural Language InferenceText GenerationControlling the Focus of Pretrained Language Generation Models
The finetuning of pretrained transformer-based language generation models are typically conducted in an end-to-end manner, where the model learns to attend to relevant parts of the input by itself. However, there does no…
Abstractive Text SummarizationResponse GenerationText GenerationNovel Chapter Abstractive Summarization using Spinal Tree Aware Sub-Sentential Content Selection
Summarizing novel chapters is a difficult task due to the length of the chapter to be summarized and the fact that summary sentences draw content from multiple sentences in the chapter. We present a pipelined extractive-…
Abstractive Text SummarizationExploring Cross-Lingual Guidance in Abstractive Summarization
Cross-lingual guidance (CLG) as an augmentation method is often applied in cross-lingual summarization (CLS) to improve its performance. In this paper, we empirically study how cross-lingual information of different qual…
Abstractive Text SummarizationAraBART: a Pretrained Arabic Sequence-to-Sequence Model for Abstractive Summarization
Like most natural language understanding and generation tasks, state-of-the-art models for summarization are transformer-based sequence-to-sequence architectures that are pretrained on large corpora. While most existing …
Abstractive Text SummarizationDecoderNatural Language UnderstandingExtraPhrase: Efficient Data Augmentation for Abstractive Summarization
Neural models trained with large amount of parallel data have achieved impressive performance in abstractive summarization tasks. However, large-scale parallel corpora are expensive and challenging to construct. In this …
Abstractive Text SummarizationData AugmentationExtractive Summarization