Challenge: Science journalism reports current scientific discoveries to non-specialists, aiming to enable public comprehension of the state of the art.
Approach: They propose a framework that integrates three LLMs mimicking the writing-reading-feedback-revision loop.
Outcome: The proposed framework generates articles that are more accessible than existing methods, including prompting single advanced models such as GPT-4 and other LLM-collaboration strategies.

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A Survey on LLMs for Story Generation (2025.findings-emnlp)

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Challenge: Methods for story generation with Large Language Models (LLMs) have come into the spotlight recently.
Approach: They propose a novel taxonomy of LLMs for story generation consisting of two major paradigms: independent story generation by an LLM, and author-assistance for story creation .
Outcome: The proposed taxonomy compares existing work on the topic with those of novel author-assistance models.
Cross-lingual Science Journalism: Select, Simplify and Rewrite Summaries for Non-expert Readers (2023.acl-long)

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Challenge: CSJ is a task of text simplification and cross-lingual scientific summarization to facilitate science journalists’ work.
Approach: They propose to combine CSJ tasks SELECT, SIMPLIFY and REWRITE to produce cross-lingual simplified science summaries for non-expert readers.
Outcome: The proposed task outperforms existing solutions on Wikipedia and can serve as a strong baseline for future work.
When science journalism meets artificial intelligence : An interactive demonstration (D18-2)

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Challenge: Existing tools for automating science journalism do not provide adequate training for AIs to be trained.
Approach: They propose an online tool that generates titles of blog titles by mimicking a human science journalist.
Outcome: The proposed tool generates blog titles by mimicking a human science journalist . it is evaluated using standard metrics to show its viability .
YESciEval: Robust LLM-as-a-Judge for Scientific Question Answering (2025.acl-long)

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Challenge: Large Language Models (LLMs) drive scientific question-answering on search engines, yet their evaluation robustness remains underexplored.
Approach: They propose an open-source framework that combines rubric-based assessment with reinforcement learning to mitigate optimism bias in LLM evaluators.
Outcome: The proposed framework combines fine-grained rubric-based assessment with reinforcement learning to mitigate optimism bias in LLM evaluators.
Explaining Mixtures of Sources in News Articles (2024.findings-emnlp)

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Challenge: a recent study shows that language models are essential for long-form article generation.
Approach: They propose a generative process where a source-selection schema is first selected by a journalist, and then sources are chosen based on categories in that schema.
Outcome: The proposed model can predict the most suitable schema given just the headline with reasonable accuracy.
‘Don’t Get Too Technical with Me’: A Discourse Structure-Based Framework for Automatic Science Journalism (2023.emnlp-main)

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Challenge: Science journalism is the production of journalistic content covering scientific topics that are not covered in the scientific literature.
Approach: They propose to use a dataset to generate a scientific paper's tuples, a summary snippet and a novel technical framework to integrate a paper' s discourse structure with its metadata to guide generation.
Outcome: The proposed system outperforms baseline methods in elaborating a content plan meaningful for the target audience, simplifying the information selected, and producing a coherent final report in a layman’s style.
Do LLMs Plan Like Human Writers? Comparing Journalist Coverage of Press Releases with LLMs (2024.emnlp-main)

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Challenge: Journalists engage in multiple steps in news writing that depend on human creativity, such as exploring different “angles” and selecting sources.
Approach: They propose to use large language models to help journalists plan their news coverage . they find that LLMs recommend more creative angles and more informational sources .
Outcome: The proposed models align better with humans when recommending angles, compared with informational sources.
CollabStory: Multi-LLM Collaborative Story Generation and Authorship Analysis (2025.findings-naacl)

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Challenge: Existing studies on LLM-LLM collaboration for open-ended tasks have focused on human-LLm interaction.
Approach: They propose to generate a dataset exclusively for LLMs to explore multi-LLM collaboration scenarios . they extend their authorship-related tasks for multi-llm settings and extend their baselines .
Outcome: The authors extend authorship-related tasks for multi-LLM settings and present baselines for LLM-LLMS collaboration.
Harnessing the power of LLMs: Evaluating human-AI text co-creation through the lens of news headline generation (2023.findings-emnlp)

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Challenge: Recent advances in Large Language Models (LLMs) have shattered the ceiling of human-like text generation.
Approach: They compared human-AI interaction types in LLM-assisted news headline generation to determine whether humans can best leverage them for writing.
Outcome: The guiding and selecting model outputs added the most benefit with the lowest cost (in time and effort) Furthermore, AI assistance did not harm participants’ perception of control compared to freeform editing.
LLMs as Meta-Reviewers’ Assistants: A Case Study (2025.naacl-long)

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Challenge: Meta-reviews are a critical step in the overall scientific peer-reviewed process, which focuses on understanding the consensus of expert opinions on a scholarly work and making informed judgments on its scientific merit.
Approach: They propose to use large language models to generate a controlled multi-perspective-summary (MPS) of their opinions to help meta-reviewers better comprehend multiple experts' perspectives.
Outcome: The proposed model can help meta-reviewers better comprehend multiple experts’ perspectives by generating a controlled multi-perspective-summary (MPS) of their opinions.

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