Papers Abstractive Text Summarization
“Abstractive Text Summarization” 태그가 달린 논문 852편 · 필터 해제
Optimizing Abstractive Summarization With Fine-Tuned PEGASUS
Abstractive text summarization is the technique of generating a short and concise summary comprising the salient ideas of a source text without making a subset of the salient sentences from the source text. The introduct…
Abstractive Text SummarizationDetect, Unlearn, Restore: Defending Text Summarization Models Against Data Poisoning
Training-time data poisoning during fine-tuning poses a significant threat to large language models (LLMs) deployed for abstractive text summarization, where small task-specific datasets exert disproportionate influence …
Abstractive Text SummarizationMASF: A Multi-Model Adaptive Selection Framework for Abstractive Text summarization
Automatic text summarization has become increasingly important due to the rapid growth of digital textual information. This paper presents a Multi-Model Adaptive Summarization Framework designed to improve the robustness…
Abstractive Text SummarizationMultiBanAbs: A Comprehensive Multi-Domain Bangla Abstractive Text Summarization Dataset
This study developed a new Bangla abstractive summarization dataset to generate concise summaries of Bangla articles from diverse sources. Most existing studies in this field have concentrated on news articles, where jou…
Abstractive Text SummarizationTransfer LearningStress Testing Factual Consistency Metrics for Long-Document Summarization
Evaluating the factual consistency of abstractive text summarization remains a significant challenge, particularly for long documents, where conventional metrics struggle with input length limitations and long-range depe…
Abstractive Text SummarizationDocument SummarizationInforME: Improving Informativeness of Abstractive Text Summarization With Informative Attention Guided by Named Entity Salience
Abstractive text summarization is integral to the Big Data era, which demands advanced methods to turn voluminous and often long text data into concise but coherent and informative summaries for efficient human consumpti…
Abstractive Text SummarizationAdvancing Decoding Strategies: Enhancements in Locally Typical Sampling for LLMs
This chapter explores advancements in decoding strategies for large language models (LLMs), focusing on enhancing the Locally Typical Sampling (LTS) algorithm. Traditional decoding methods, such as top-k and nucleus samp…
Abstractive Text SummarizationComputational EfficiencyDiversityStory Generation+1ARC: Argument Representation and Coverage Analysis for Zero-Shot Long Document Summarization with Instruction Following LLMs
Integrating structured information has long improved the quality of abstractive summarization, particularly in retaining salient content. In this work, we focus on a specific form of structure: argument roles, which are …
Abstractive Text SummarizationARCArticlesDocument Summarization+1Power-Law Decay Loss for Large Language Model Finetuning: Focusing on Information Sparsity to Enhance Generation Quality
During the finetuning stage of text generation tasks, standard cross-entropy loss treats all tokens equally. This can lead models to overemphasize high-frequency, low-information tokens, neglecting lower-frequency tokens…
Abstractive Text SummarizationInformativenessLanguage ModelingLanguage Modelling+4Enhancing Abstractive Summarization of Scientific Papers Using Structure Information
Abstractive summarization of scientific papers has always been a research focus, yet existing methods face two main challenges. First, most summarization models rely on Encoder-Decoder architectures that treat papers as …
Abstractive Text SummarizationFeature EngineeringLow-Resource Language Processing: An OCR-Driven Summarization and Translation Pipeline
This paper presents an end-to-end suite for multilingual information extraction and processing from image-based documents. The system uses Optical Character Recognition (Tesseract) to extract text in languages such as En…
Abstractive Text SummarizationLanguage ModelingLanguage ModellingLarge Language Model+5ProdRev: A DNN framework for empowering customers using generative pre-trained transformers
Following the pandemic, customers, preference for using e-commerce has accelerated. Since much information is available in multiple reviews (sometimes running in thousands) for a single product, it can create decision pa…
Abstractive Text SummarizationCommon Sense ReasoningA Split-then-Join Approach to Abstractive Summarization for Very Long Documents in a Low Resource Setting
$\texttt{BIGBIRD-PEGASUS}$ model achieves $\textit{state-of-the-art}$ on abstractive text summarization for long documents. However it's capacity still limited to maximum of $4,096$ tokens, thus caused performance degrad…
Abstractive Text SummarizationText SummarizationTransfer LearningGASCADE: Grouped Summarization of Adverse Drug Event for Enhanced Cancer Pharmacovigilance
In the realm of cancer treatment, summarizing adverse drug events (ADEs) reported by patients using prescribed drugs is crucial for enhancing pharmacovigilance practices and improving drug-related decision-making. While …
Abstractive Text SummarizationDecision MakingDecoderPharmacovigilanceAIstorian lets AI be a historian: A KG-powered multi-agent system for accurate biography generation
Huawei has always been committed to exploring the AI application in historical research. Biography generation, as a specialized form of abstractive summarization, plays a crucial role in historical research but faces uni…
Abstractive Text SummarizationChunkingData AugmentationHallucination+4ARLED: Leveraging LED-based ARMAN Model for Abstractive Summarization of Persian Long Documents
The increasing volume of textual data poses challenges in reading and comprehending large documents, particularly for scholars who need to extract useful information from research articles. Automatic text summarization h…
Abstractive Text SummarizationArticlesText SummarizationA Hybrid Architecture with Efficient Fine Tuning for Abstractive Patent Document Summarization
Automatic patent summarization approaches that help in the patent analysis and comprehension procedure are in high demand due to the colossal growth of innovations. The development of natural language processing (NLP), t…
Abstractive Text SummarizationDocument SummarizationDomain GeneralizationMeta-Learning+1Advancements in Natural Language Processing for Automatic Text Summarization
The substantial growth of textual content in diverse domains and platforms has led to a considerable need for Automatic Text Summarization (ATS) techniques that aid in the process of text analysis. The effectiveness of t…
Abstractive Text SummarizationExtractive SummarizationText GenerationText SummarizationBRIDO: Bringing Democratic Order to Abstractive Summarization
Hallucination refers to the inaccurate, irrelevant, and inconsistent text generated from large language models (LLMs). While the LLMs have shown great promise in a variety of tasks, the issue of hallucination still remai…
Abstractive Text SummarizationContrastive LearningHallucinationText SummarizationCSTRL: Context-Driven Sequential Transfer Learning for Abstractive Radiology Report Summarization
A radiology report comprises several sections, including the Findings and Impression of the diagnosis. Automatically generating the Impression from the Findings is crucial for reducing radiologists' workload and improvin…
Abstractive Text SummarizationDiagnosticTransfer Learning