Papers Abstractive Text Summarization
“Abstractive Text Summarization” 태그가 달린 논문 852편 · 필터 해제
Clinfo.ai: An Open-Source Retrieval-Augmented Large Language Model System for Answering Medical Questions using Scientific Literature
The quickly-expanding nature of published medical literature makes it challenging for clinicians and researchers to keep up with and summarize recent, relevant findings in a timely manner. While several closed-source sum…
Abstractive Text SummarizationInformation RetrievalLanguage ModelingLanguage Modelling+2PartialFormer: Modeling Part Instead of Whole for Machine Translation
The design choices in Transformer feed-forward neural networks have resulted in significant computational and parameter overhead. In this work, we emphasize the importance of hidden dimensions in designing lightweight FF…
Abstractive Text SummarizationMachine TranslationTranslationFidelity-Enriched Contrastive Search: Reconciling the Faithfulness-Diversity Trade-Off in Text Generation
In this paper, we address the hallucination problem commonly found in natural language generation tasks. Language models often generate fluent and convincing content but can lack consistency with the provided source, res…
Abstractive Text SummarizationDialogue GenerationDiversityHallucination+3Enhancing Abstractiveness of Summarization Models through Calibrated Distillation
Sequence-level knowledge distillation reduces the size of Seq2Seq models for more efficient abstractive summarization. However, it often leads to a loss of abstractiveness in summarization. In this paper, we propose a no…
Abstractive Text SummarizationInformativenessKnowledge DistillationOn Context Utilization in Summarization with Large Language Models
Large language models (LLMs) excel in abstractive summarization tasks, delivering fluent and pertinent summaries. Recent advancements have extended their capabilities to handle long-input contexts, exceeding 100k tokens.…
Abstractive Text SummarizationPositionQuestion AnsweringMetric Ensembles For Hallucination Detection
Abstractive text summarization has garnered increased interest as of late, in part due to the proliferation of large language models (LLMs). One of the most pressing problems related to generation of abstractive summarie…
Abstractive Text SummarizationHallucinationText SummarizationKCTS: Knowledge-Constrained Tree Search Decoding with Token-Level Hallucination Detection
Large Language Models (LLMs) have demonstrated remarkable human-level natural language generation capabilities. However, their potential to generate misinformation, often called the hallucination problem, poses a signifi…
Abstractive Text SummarizationHallucinationMisinformationText GenerationCalibrating Likelihoods towards Consistency in Summarization Models
Despite the recent advances in abstractive text summarization, current summarization models still suffer from generating factually inconsistent summaries, reducing their utility for real-world application. We argue that …
Abstractive Text SummarizationNatural Language InferenceText SummarizationTeaching Language Models to Hallucinate Less with Synthetic Tasks
Large language models (LLMs) frequently hallucinate on abstractive summarization tasks such as document-based question-answering, meeting summarization, and clinical report generation, even though all necessary informati…
Abstractive Text SummarizationHallucinationMeeting SummarizationQuestion Answering+1Improving Summarization with Human Edits
Recent work has shown the promise of learning with human feedback paradigms to produce human-determined high-quality text. Existing works use human feedback to train large language models (LLMs) in general domain abstrac…
Abstractive Text SummarizationAbstractive Summarization of Large Document Collections Using GPT
This paper proposes a method of abstractive summarization designed to scale to document collections instead of individual documents. Our approach applies a combination of semantic clustering, document size reduction with…
Abstractive Text SummarizationChunkingDocument SummarizationLow Resource Summarization using Pre-trained Language Models
With the advent of Deep Learning based Artificial Neural Networks models, Natural Language Processing (NLP) has witnessed significant improvements in textual data processing in terms of its efficiency and accuracy. Howev…
Abstractive Text SummarizationTowards Green AI in Fine-tuning Large Language Models via Adaptive Backpropagation
Fine-tuning is the most effective way of adapting pre-trained large language models (LLMs) to downstream applications. With the fast growth of LLM-enabled AI applications and democratization of open-souced LLMs, fine-tun…
Abstractive Text SummarizationBenLLMEval: A Comprehensive Evaluation into the Potentials and Pitfalls of Large Language Models on Bengali NLP
Large Language Models (LLMs) have emerged as one of the most important breakthroughs in NLP for their impressive skills in language generation and other language-specific tasks. Though LLMs have been evaluated in various…
Abstractive Text SummarizationNatural Language InferenceQuestion AnsweringSentiment Analysis+5NumHG: A Dataset for Number-Focused Headline Generation
Headline generation, a key task in abstractive summarization, strives to condense a full-length article into a succinct, single line of text. Notably, while contemporary encoder-decoder models excel based on the ROUGE me…
Abstractive Text SummarizationArticlesDecoderHeadline Generation+1Benchmarking the Generation of Fact Checking Explanations
Fighting misinformation is a challenging, yet crucial, task. Despite the growing number of experts being involved in manual fact-checking, this activity is time-consuming and cannot keep up with the ever-increasing amoun…
Abstractive Text SummarizationArticlesBenchmarkingFact Checking+2Inducing Causal Structure for Abstractive Text Summarization
The mainstream of data-driven abstractive summarization models tends to explore the correlations rather than the causal relationships. Among such correlations, there can be spurious ones which suffer from the language pr…
Abstractive Text SummarizationText SummarizationPromptSum: Parameter-Efficient Controllable Abstractive Summarization
Prompt tuning (PT), a parameter-efficient technique that only tunes the additional prompt embeddings while keeping the backbone pre-trained language model (PLM) frozen, has shown promising results in language understandi…
Abstractive Text SummarizationLanguage ModelingLanguage ModellingESRL: Efficient Sampling-based Reinforcement Learning for Sequence Generation
Applying Reinforcement Learning (RL) to sequence generation models enables the direct optimization of long-term rewards (\textit{e.g.,} BLEU and human feedback), but typically requires large-scale sampling over a space o…
Abstractive Text SummarizationLanguage ModelingLanguage ModellingLarge Language Model+5Retrieval Augmented Generation and Representative Vector Summarization for large unstructured textual data in Medical Education
Large Language Models are increasingly being used for various tasks including content generation and as chatbots. Despite their impressive performances in general tasks, LLMs need to be aligned when applying for domain s…
Abstractive Text SummarizationHallucinationRAGRetrieval+1