WeChat Mini Program
Old Version Features

    • MedTSS: Transforming Abstractive Summarization of Scientific Articles with Linguistic Analysis and Concept Reinforcement

    Nadia Saeed,Hammad Naveed

    KNOWLEDGE AND INFORMATION SYSTEMS（2024）

    National University of Computer and Emerging Sciences (NUCES-FAST)

    Cited 0|Views9
    Abstract
    This research addresses the limitations of pretrained models (PTMs) in generating accurate and comprehensive abstractive summaries for scientific articles, with a specific focus on the challenges posed by medical research. The proposed solution named medical text simplification and summarization (MedTSS) introduces a dedicated module designed to enrich source text for PTMs. MedTSS addresses issues related to token limits, reinforces multiple concepts, and mitigates entity hallucination problems without necessitating additional training. Furthermore, the module conducts linguistic analysis to simplify generated summaries, particularly tailored for the complex nature of medical research articles. The results demonstrate a significant enhancement, with MedTSS improving the Rouge-1 score from 16.46 to 35.17 without requiring additional training. By emphasizing knowledge-driven components, this framework offers a distinct perspective, challenging the common narrative of ’more data’ or ’more parameters.’ This alternative approach, especially applicable in health-related domains, signifies a broader contribution to the field of NLP. MedTSS serves as an innovative model that not only addresses the intricacies of medical research summarization but also presents a paradigm shift with implications for diverse domains beyond its initial scope.
    More
    Translated text
    Key words
    Scientific journal articles,Summarization, data processing,Pre-trained models,Simplification,Entity hallucination
    求助PDF
    上传PDF
    View via Publisher
    Bibtex
    AI Read Science
    AI Summary
    AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
    Example
    Background
    Key content
    Introduction
    Methods
    Results
    Related work
    Fund
    Key content
    • Pretraining has recently greatly promoted the development of natural language processing (NLP)
    • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
    • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
    • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
    • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
    Upload PDF to Generate Summary
    Must-Reading Tree
    Example
    Generate MRT to find the research sequence of this paper
    Data Disclaimer
    The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
    Chat Paper
    Summary is being generated by the instructions you defined