Papers News Summarization
“News Summarization” 태그가 달린 논문 85편 · 필터 해제
APE: A Data-Centric Benchmark for Efficient LLM Adaptation in Text Summarization
We present Adjacent Possible Exploration (APE), a simple yet effective method for adapting large language models to specific tasks using minimal computational resources. Unlike traditional fine-tuning that requires exten…
GPUNews SummarizationText SummarizationA Python Tool for Reconstructing Full News Text from GDELT
News data have become an essential resource across various disciplines, including economics, finance, management, social sciences, and computer science. Researchers leverage newspaper articles to study economic trends, m…
ArticlesFake News DetectionNews SummarizationSentiment AnalysisEvaluating the Effectiveness of Large Language Models in Automated News Article Summarization
The automation of news analysis and summarization presents a promising solution to the challenge of processing and analyzing vast amounts of information prevalent in today's information society. Large Language Models (LL…
News SummarizationLearning to Partially Defer for Sequences
In the Learning to Defer (L2D) framework, a prediction model can either make a prediction or defer it to an expert, as determined by a rejector. Current L2D methods train the rejector to decide whether to reject the enti…
News SummarizationPredictionEvaluating Small Language Models for News Summarization: Implications and Factors Influencing Performance
The increasing demand for efficient summarization tools in resource-constrained environments highlights the need for effective solutions. While large language models (LLMs) deliver superior summarization quality, their h…
News SummarizationUnraveling the Capabilities of Language Models in News Summarization
Given the recent introduction of multiple language models and the ongoing demand for improved Natural Language Processing tasks, particularly summarization, this work provides a comprehensive benchmarking of 20 recent la…
BenchmarkingFew-Shot LearningIn-Context LearningNews Summarization+2Multi-round, Chain-of-thought Post-editing for Unfaithful Summaries
Recent large language models (LLMs) have demonstrated a remarkable ability to perform natural language understanding and generation tasks. In this work, we investigate the use of LLMs for evaluating faithfulness in news …
Natural Language UnderstandingNews SummarizationExplainable News Summarization -- Analysis and mitigation of Disagreement Problem
Explainable AI (XAI) techniques for text summarization provide valuable understanding of how the summaries are generated. Recent studies have highlighted a major challenge in this area, known as the disagreement problem.…
Extreme SummarizationNews SummarizationText SummarizationA Mixed-Language Multi-Document News Summarization Dataset and a Graphs-Based Extract-Generate Model
Existing research on news summarization primarily focuses on single-language single-document (SLSD), single-language multi-document (SLMD) or cross-language single-document (CLSD). However, in real-world scenarios, news …
News SummarizationGlobeSumm: A Challenging Benchmark Towards Unifying Multi-lingual, Cross-lingual and Multi-document News Summarization
News summarization in today's global scene can be daunting with its flood of multilingual content and varied viewpoints from different sources. However, current studies often neglect such real-world scenarios as they ten…
Document SummarizationMulti-Document SummarizationNews SummarizationBeyond Model Collapse: Scaling Up with Synthesized Data Requires Verification
Large Language Models (LLM) are increasingly trained on data generated by other LLM, either because generated text and images become part of the pre-training corpus, or because synthetized data is used as a replacement f…
News SummarizationEnhancing News Summarization with ELearnFit through Efficient In-Context Learning and Efficient Fine-Tuning
With the deluge of information delivered by the daily news cycle, there is a growing need to effectively and efficiently summarize news feeds for quick consumption. We leverage large language models (LLMs), with their ad…
ArticlesFew-Shot LearningIn-Context LearningNews Summarization+1Understanding Position Bias Effects on Fairness in Social Multi-Document Summarization
Text summarization models have typically focused on optimizing aspects of quality such as fluency, relevance, and coherence, particularly in the context of news articles. However, summarization models are increasingly be…
ArticlesDocument SummarizationFairnessMulti-Document Summarization+3Generating Faithful and Complete Hospital-Course Summaries from the Electronic Health Record
The rapid adoption of Electronic Health Records (EHRs) has been instrumental in streamlining administrative tasks, increasing transparency, and enabling continuity of care across providers. An unintended consequence of t…
Domain AdaptationNews SummarizationSentenceConcurrent Linguistic Error Detection (CLED) for Large Language Models
The wide adoption of Large language models (LLMs) makes their dependability a pressing concern. Detection of errors is the first step to mitigating their impact on a system and thus, efficient error detection for LLMs is…
News SummarizationvalidGerman also Hallucinates! Inconsistency Detection in News Summaries with the Absinth Dataset
The advent of Large Language Models (LLMs) has led to remarkable progress on a wide range of natural language processing tasks. Despite the advances, these large-sized models still suffer from hallucinating information i…
HallucinationIn-Context LearningNews SummarizationText SummarizationHow Much Annotation is Needed to Compare Summarization Models?
Modern instruction-tuned models have become highly capable in text generation tasks such as summarization, and are expected to be released at a steady pace. In practice one may now wish to choose confidently, but with mi…
News SummarizationText GenerationTofuEval: Evaluating Hallucinations of LLMs on Topic-Focused Dialogue Summarization
Single document news summarization has seen substantial progress on faithfulness in recent years, driven by research on the evaluation of factual consistency, or hallucinations. We ask whether these advances carry over t…
HallucinationNews SummarizationSentenceText SummarizationContext-aware Decoding Reduces Hallucination in Query-focused Summarization
Query-focused summarization (QFS) aims to provide a summary of a single document/multi documents that can satisfy the information needs of a given query. It is useful for various real-world applications, such as abstract…
HallucinationLanguage ModellingLarge Language ModelNews Summarization+4Shaping Political Discourse using multi-source News Summarization
Multi-document summarization is the process of automatically generating a concise summary of multiple documents related to the same topic. This summary can help users quickly understand the key information from a large c…
Document SummarizationMulti-Document SummarizationNews Summarization