Papers Abstractive Text Summarization
“Abstractive Text Summarization” 태그가 달린 논문 852편 · 필터 해제
Domain Adaptation with Pre-trained Transformers for Query Focused Abstractive Text Summarization
The Query Focused Text Summarization (QFTS) task aims at building systems that generate the summary of the text document(s) based on the given query. A key challenge in addressing this task is the lack of large labeled d…
Abstractive Text SummarizationDomain AdaptationText SummarizationTransfer Learning+1Adaptive Beam Search to Enhance On-device Abstractive Summarization
We receive several essential updates on our smartphones in the form of SMS, documents, voice messages, etc. that get buried beneath the clutter of content. We often do not realize the key information without going throug…
Abstractive Text SummarizationKnowledge DistillationMay the Force Be with Your Copy Mechanism: Enhanced Supervised-Copy Method for Natural Language Generation
Recent neural sequence-to-sequence models with a copy mechanism have achieved remarkable progress in various text generation tasks. These models addressed out-of-vocabulary problems and facilitated the generation of rare…
Abstractive Text SummarizationData-to-Text GenerationText GenerationCrossSum: Beyond English-Centric Cross-Lingual Summarization for 1,500+ Language Pairs
We present CrossSum, a large-scale cross-lingual summarization dataset comprising 1.68 million article-summary samples in 1,500+ language pairs. We create CrossSum by aligning parallel articles written in different langu…
Abstractive Text SummarizationArticlesCross-Lingual Abstractive SummarizationRetrieval+1Learning Rich Representation of Keyphrases from Text
In this work, we explore how to train task-specific language models aimed towards learning rich representation of keyphrases from text documents. We experiment with different masking strategies for pre-training transform…
Abstractive Text SummarizationKeyphrase ExtractionKeyphrase Generationnamed-entity-recognition+5LongT5: Efficient Text-To-Text Transformer for Long Sequences
Recent work has shown that either (1) increasing the input length or (2) increasing model size can improve the performance of Transformer-based neural models. In this paper, we present a new model, called LongT5, with wh…
Abstractive Text SummarizationLong-range modelingMulti-Document SummarizationQuestion Answering+1Reinforced Abstractive Summarization with Adaptive Length Controlling
Document summarization, as a fundamental task in natural language generation, aims to generate a short and coherent summary for a given document. Controllable summarization, especially of the length, is an important issu…
Abstractive Text SummarizationDocument SummarizationSaliency PredictionSentence+1CO2Sum:Contrastive Learning for Factual-Consistent Abstractive Summarization
Generating factual-consistent summaries is a challenging task for abstractive summarization. Previous works mainly encode factual information or perform post-correct/rank after decoding. In this paper, we provide a factu…
Abstractive Text SummarizationContrastive LearningDecoderControlling Conditional Language Models without Catastrophic Forgetting
Machine learning is shifting towards general-purpose pretrained generative models, trained in a self-supervised manner on large amounts of data, which can then be applied to solve a large number of tasks. However, due to…
Abstractive Text SummarizationCode GenerationAbstractive Text Summarization: Enhancing Sequence-to-Sequence Models Using Word Sense Disambiguation and Semantic Content Generalization
Abstract Nowadays, most research conducted in the field of abstractive text summarization focuses on neural-based models alone, without considering their combination with knowledge-based approaches that could further enh…
Abstractive Text Summarizationnamed-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)+4New Approaches to Long Document Summarization: Fourier Transform Based Attention in a Transformer Model
In this work, we extensively redesign the newly introduced method of token mixing using Fourier Transforms (FNET) to replace the computationally expensive self-attention mechanism in a full transformer implementation on …
Abstractive Text SummarizationDecoderDocument SummarizationPointer over Attention: An Improved Bangla Text Summarization Approach Using Hybrid Pointer Generator Network
Despite the success of the neural sequence-to-sequence model for abstractive text summarization, it has a few shortcomings, such as repeating inaccurate factual details and tending to repeat themselves. We propose a hybr…
Abstractive Text SummarizationArticlesText SummarizationImproving the Faithfulness of Abstractive Summarization via Entity Coverage Control
Abstractive summarization systems leveraging pre-training language models have achieved superior results on benchmark datasets. However, such models have been shown to be more prone to hallucinate facts that are unfaithf…
Abstractive Text SummarizationControlling Pretrained Language Generation Models by Learning to Focus
Transformer-based language models, which are pretrained on large-scale unsupervised data and then finetuned on task-specific datasets, have become the dominant paradigm for various natural language generation tasks. The …
Abstractive Text SummarizationResponse GenerationText GenerationMoFE: Mixture of Factual Experts for Controlling Hallucinations in Abstractive Summarization
Neural abstractive summarization models are susceptible to generating factually inconsistent content, a phenomenon known as hallucination. This limits the usability and adoption of these systems in real-world application…
Abstractive Text SummarizationHallucinationSummaReranker: A Multi-Task Mixture-of-Experts Re-ranking Framework for Abstractive Summarization
Sequence-to-sequence neural networks have recently achieved great success in abstractive summarization, especially with the trend of fine-tuning large pre-trained language models on the downstream dataset. These models a…
Abstractive Text SummarizationMixture-of-ExpertsRe-RankingSumm$^N$: 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 SummarizationA Zero-Resource Approach to Cross-Lingual Query-Focused Abstractive Summarization
We present a novel approach for cross-lingual query-focused abstractive summarization (QFAS) that leverages the translate-then-summarize paradigm. We approach cross-lingual QFAS as a zero-resource problem and introduce a…
Abstractive Text SummarizationDecoderTranslationFRSUM: Towards Faithful Abstractive Summarization via Enhancing Factual Robustness
Though current Seq2Seq summarization models are capable of generating fluent and grammatical summaries, they are still suffering from the unfaithful generation problem.In this paper, we study the faithfulness of existing…
Abstractive Text SummarizationGeneration of News Articles from Tweets : An Experiment
With millions of users, Twitter is among the most popular social media platforms today, making it one of the largest sources of continuous real-time information on the Internet. Users frequently broadcast events occurrin…
Abstractive Text SummarizationArticles