Hugging Face Trending Papers July 21, 2026

AutoJourn: Multi-Perspective Summarisation, Bias Detection and Bias Neutralisation for LLM-Generated News in Automated Journalism

Read the original on Hugging Face Trending Papers →

We present AutoJourn, a demonstration system for multi-perspective news generation and bias-aware evaluation using large language models (LLMs). The system tackles three core challenges in responsible automated journalism: extracting diverse perspectives from unstructured social media discussions, generating summaries that preserve viewpoint diversity, and detecting or mitigating bias in AI-generated news.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at Hugging Face Trending Papers.

arXiv AI

Jul 22

AutoJourn: Multi-Perspective Summarisation, Bias Detection and Bias Neutralisation for LLM-Generated News in Automated Journalism

arXiv:2607. 18983v1 Announce Type: cross Abstract: We present AutoJourn, a demonstration system for multi-perspective news generation and bias-aware evaluation using large language models (LLMs).

By Himel Ghosh, Ahmed Mosharafa, Georg Groh

llms safety

Hugging Face Trending Papers

Jun 22

UnBias-Plus: Detect, Explain, and Rewrite Bias

Bias in natural language remains a persistent challenge in both human-written and AI-generated content, affecting domains such as journalism, education, and AI research. Most existing detection methods identify only the presence of bias, with limited support for granular detection, interpretable explanations, neutral rewriting, and openly available trained models.

safety

arXiv AI

Aug 5

Decoupling Generation and Selection for Budget-Constrained Faithful Summarization

arXiv:2608. 03655v1 Announce Type: cross Abstract: Abstractive summarization models remain vulnerable to factual inconsistency, redundancy, and weak length control.

By Zeyu Wang, Guanghua Wang, Meng Xu

nlp

arXiv AI

Jun 6

MASF: A Multi-Model Adaptive Selection Framework for Abstractive Text summarization

arXiv:2606. 05494v1 Announce Type: cross Abstract: Automatic text summarization has become increasingly important due to the rapid growth of digital textual information.

By Ahmed Alansary, Ali Hamdi

llms nlp fine-tuning

arXiv AI

Jul 14

A Multi-Model Metric-based Selection Framework for Abstractive Text summarization

arXiv:2606. 05494v3 Announce Type: replace-cross Abstract: Automatic text summarization has become increasingly important due to the rapid growth of digital textual information.

By Ahmed Alansary, Ali Hamdi

llms nlp fine-tuning

arXiv AI

Jul 14

Large Language Models for Token-Efficient and Semantic-Preserving Opinion Summarization

arXiv:2607. 10825v1 Announce Type: cross Abstract: Opinionated text - spanning product reviews, hotel feedback, and social posts - captures rich signals about user experiences, preferences, and concerns.

By Fabrizio Marozzo, Stefano Iannicelli

llms nlp

One email a morning, machine-written

Related stories

AutoJourn: Multi-Perspective Summarisation, Bias Detection and Bias Neutralisation for LLM-Generated News in Automated Journalism

UnBias-Plus: Detect, Explain, and Rewrite Bias

Decoupling Generation and Selection for Budget-Constrained Faithful Summarization

MASF: A Multi-Model Adaptive Selection Framework for Abstractive Text summarization

A Multi-Model Metric-based Selection Framework for Abstractive Text summarization

Large Language Models for Token-Efficient and Semantic-Preserving Opinion Summarization