Papers Abstractive Text Summarization
“Abstractive Text Summarization” 태그가 달린 논문 852편 · 필터 해제
EASE: Extractive-Abstractive Summarization End-to-End using the Information Bottleneck Principle
Current abstractive summarization systems outperform their extractive counterparts, but their widespread adoption is inhibited by the inherent lack of interpretability. Extractive summarization systems, though interpreta…
Abstractive Text SummarizationExtractive SummarizationText GenerationJust Ask! Evaluating Machine Translation by Asking and Answering Questions
In this paper, we show that automatically-generated questions and answers can be used to evaluate the quality of Machine Translation (MT) systems. Building on recent work on the evaluation of abstractive text summarizati…
Abstractive Text SummarizationMachine TranslationText SummarizationTranslations2s-ft: Fine-Tuning Pretrained Transformer Encoders for Sequence-to-Sequence Learning
Pretrained bidirectional Transformers, such as BERT, have achieved significant improvements in a wide variety of language understanding tasks, while it is not straightforward to directly apply them for natural language g…
Abstractive Text SummarizationQuestion GenerationQuestion-GenerationText GenerationTowards Making the Most of Multilingual Pretraining for Zero-Shot Neural Machine Translation
This paper demonstrates that multilingual pretraining and multilingual fine-tuning are both critical for facilitating cross-lingual transfer in zero-shot translation, where the neural machine translation (NMT) model is t…
Abstractive Text SummarizationCross-Lingual Abstractive SummarizationCross-Lingual TransferDecoder+4PRIMERA: Pyramid-based Masked Sentence Pre-training for Multi-document Summarization
We introduce PRIMERA, a pre-trained model for multi-document representation with a focus on summarization that reduces the need for dataset-specific architectures and large amounts of fine-tuning labeled data. PRIMERA us…
Abstractive Text SummarizationDecoderDocument SummarizationMulti-Document Summarization+2Summ^N: A Multi-Stage Summarization Framework for Long Input Dialogues and Documents
Text summarization helps readers capture salient information from documents, news, interviews, and meetings. However, most state-of-the-art pretrained language models (LM) are unable to efficiently process long text for …
Abstractive Text SummarizationDocument SummarizationMeeting SummarizationText SummarizationShould We Trust This Summary? Bayesian Abstractive Summarization to The Rescue
We explore the notion of uncertainty in the context of modern abstractive summarization models, using the tools of Bayesian Deep Learning. Our approach approximates Bayesian inference by first extending state-of-the-art …
Abstractive Text SummarizationActive LearningBayesian InferenceODE Transformer: An Ordinary Differential Equation-Inspired Model for Sequence Generation
Residual networks are an Euler discretization of solutions to Ordinary Differential Equations (ODE). This paper explores a deeper relationship between Transformer and numerical ODE methods. We first show that a residual …
Abstractive Text SummarizationMachine TranslationTranslationHallucinated but Factual! Inspecting the Factuality of Hallucinations in Abstractive Summarization
State-of-the-art abstractive summarization systems often generate hallucinations; i.e., content that is not directly inferable from the source text. Despite being assumed to be incorrect, we find that much hallucinated c…
Abstractive Text SummarizationReinforcement Learning (RL)World KnowledgeLearning Rich Representation of Keyphrases from Text
In this work, we explore how to learn task-specific language models aimed towards learning rich representation of keyphrases from text documents. We experiment with different masking strategies for training transformer l…
Abstractive Text SummarizationKeyphrase ExtractionKeyphrase Generationnamed-entity-recognition+5Attention Temperature Matters in Abstractive Summarization Distillation
Recent progress of abstractive text summarization largely relies on large pre-trained sequence-to-sequence Transformer models, which are computationally expensive. This paper aims to distill these large models into small…
Abstractive Text SummarizationText SummarizationFaithful or Extractive? On Mitigating the Faithfulness-Abstractiveness Trade-off in Abstractive Summarization
Despite recent progress in abstractive summarization, systems still suffer from faithfulness errors. While prior work has proposed models that improve faithfulness, it is unclear whether the improvement comes from an inc…
Abstractive Text SummarizationDYLE: Dynamic Latent Extraction for Abstractive Long-Input Summarization
Transformer-based models have achieved state-of-the-art performance on short-input summarization. However, they still struggle with summarizing longer text. In this paper, we present DYLE, a novel dynamic latent extracti…
Abstractive Text SummarizationText SummarizationModeling Endorsement for Multi-Document Abstractive Summarization
A crucial difference between single- and multi-document summarization is how salient content manifests itself in the document(s). While such content may appear at the beginning of a single document, essential information…
Abstractive Text SummarizationDecoderDocument SummarizationMulti-Document SummarizationControl Prefixes for Parameter-Efficient Text Generation
Prefix-tuning is a powerful lightweight technique for adapting a large pre-trained language model to a downstream application. However, it uses the same dataset-level tuned prompt for all examples in the dataset. We exte…
Abstractive Text SummarizationAttributeData-to-Text GenerationLanguage Modeling+4CaPE: Contrastive Parameter Ensembling for Reducing Hallucination in Abstractive Summarization
Hallucination is a known issue for neural abstractive summarization models. Recent work suggests that the degree of hallucination may depend on errors in the training data. In this work, we propose a new method called Co…
Abstractive Text SummarizationARCHallucinationInformativenessEnhance Long Text Understanding via Distilled Gist Detector from Abstractive Summarization
Long text understanding is important yet challenging in natural language processing. A long article or essay usually contains many redundant words that are not pertinent to its gist and sometimes can be regarded as noise…
Abstractive Text SummarizationDocument ClassificationOpen-Domain Question AnsweringQuestion Answering+2VieSum: How Robust Are Transformer-based Models on Vietnamese Summarization?
Text summarization is a challenging task within natural language processing that involves text generation from lengthy input sequences. While this task has been widely studied in English, there is very limited research o…
Abstractive Text SummarizationDecoderSelf-Supervised LearningText Generation+3HydraSum: Disentangling Stylistic Features in Text Summarization using Multi-Decoder Models
Summarization systems make numerous "decisions" about summary properties during inference, e.g. degree of copying, specificity and length of outputs, etc. However, these are implicitly encoded within model parameters and…
Abstractive Text SummarizationDecoderMixture-of-ExpertsSpecificity+1HowSumm: A Multi-Document Summarization Dataset Derived from WikiHow Articles
We present HowSumm, a novel large-scale dataset for the task of query-focused multi-document summarization (qMDS), which targets the use-case of generating actionable instructions from a set of sources. This use-case is …
Abstractive Text SummarizationArticlesDocument SummarizationMulti-Document Summarization