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.

Similar Papers

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 .
Longform Multimodal Lay Summarization of Scientific Papers: Towards Automatically Generating Science Blogs from Research Articles (2024.lrec-main)

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Challenge: Science blogs and lay-speak are critical to communicating scientific information to the general public and policymakers.
Approach: They propose to use presentation transcripts and slides to generate a scientific blog from a research article in layperson's terms.
Outcome: The proposed approach can generate a blog text and select the most relevant figures to explain a research article in layperson’s terms, essentially a science blog.
SciNews: From Scholarly Complexities to Public Narratives – a Dataset for Scientific News Report Generation (2024.lrec-main)

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Challenge: Scientific news reports are a bridge between academic and scientific publications . however, the pursuit of automated news reports faces challenges due to the insufficient availability of parallel corpora.
Approach: They propose to use a corpus of scientific news reports to facilitate this paradigm development . they highlight the divergences in readability and brevity between scientific news narratives and academic manuscripts .
Outcome: The proposed corpus includes academic publications and scientific news reports across nine disciplines.
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.
Making Science Simple: Corpora for the Lay Summarisation of Scientific Literature (2022.emnlp-main)

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Challenge: Existing datasets for lay summarisation are limited in size and scope, hindering the development of data-driven approaches.
Approach: They propose to use two new datasets for the lay summarisation of biomedical research articles to characterise their lay summaries.
Outcome: The proposed datasets are compared with existing datasets and show they can be leveraged to support different audiences and applications.
HarriGT: A Tool for Linking News to Science (P18-4)

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Challenge: citation-based metrics are not as transparent as once thought, says a new study . citation metrics are a key component of measuring scientific impact in society .
Approach: They propose a tool to build corpora of news articles linked to scientific papers . citation-based metrics have catalysed research funding councils' interest in impact .
Outcome: The proposed tool can build corpora of news articles linked to scientific papers . it integrates with 3 large external citation networks to surface relevant examples of scientific literature .
Discourse as a Function of Event: Profiling Discourse Structure in News Articles around the Main Event (2020.acl-main)

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Challenge: a recent study shows that news articles report context-informing content that is not necessarily relevant to main events.
Approach: They propose to use a functional discourse structure for news articles to model news content structures . they propose to integrate system predicted news structures into the annotations .
Outcome: The proposed model outperforms existing models in event coreference resolution.
A Summarization System for Scientific Documents (D19-3)

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Challenge: a qualitative user study identified the most valuable scenarios for scientific content consumption.
Approach: They propose a system that retrieves and summarizes scientific documents for a given information need.
Outcome: The proposed system ingested 270,000 scientific papers and validated with human experts.
Large Language Models for Scientific Information Extraction: An Empirical Study for Virology (2024.findings-eacl)

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Challenge: Scholarly communication in the digital age is facing significant challenges due to the overwhelming volume of publications.
Approach: They propose to use Wikipedia infoboxes and structured Amazon product descriptions to create structured scholarly contribution summaries using text generation capabilities of LLMs.
Outcome: The proposed model can be applied to complex IE tasks within terse domains like Science with 1000x fewer parameters than the state-of-the-art GPT-davinci.
Beyond Metadata: What Paper Authors Say About Corpora They Use (2021.findings-acl)

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Challenge: Currently, dataset retrieval relies almost exclusively on metadata provided by the publishers.
Approach: They propose to use metadata to extract review statements from scientific publications . they argue that a crucial piece of information is missing to inform the examination of search results .
Outcome: The proposed analysis is the first of its kind in the field of Natural Language Processing.

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