IDEAS home Printed from https://ideas.repec.org/a/spr/jcsosc/v8y2025i1d10.1007_s42001-024-00352-w.html
   My bibliography  Save this article

Prefix tuning with prompt augmentation for efficient financial news summarization

Author

Listed:
  • Shangyang Mou

    (Kobe University)

  • Qiang Xue

    (Kobe University)

  • Xunquan Chen

    (Kobe University)

  • Jinhui Chen

    (Wakayama University)

  • Ryoichi Takashima

    (Kobe University)

  • Tetsuya Takiguchi

    (Kobe University)

  • Yasuo Ariki

    (Kobe University)

Abstract

In financial markets, the sentiment expressed in news articles plays a pivotal role in interpreting and forecasting market trends, which also holds true for the task of financial news summarization (FNS). Leveraging AI models to analyze social science data, this paper employs financial sentiment to improve FNS effectiveness by introducing a novel method that combines the sentiment polarity extracted from financial news with prompt augmentation techniques to ensure that the generated summaries are emotionally consistent with the source articles. Specifically, the detected sentiments are embedded into prompts and provide directive instructions to the model to generate summaries. Furthermore, to address the problem of limited large-scale datasets for FNS and ensure more tailored results, we employed prefix tuning as a fine-tuning strategy. Preliminary results indicate that our combined methodology outperforms approaches that use only prefix tuning. The experimental findings further validate the significance of sentiment analysis in FNS, which enhances the accuracy of capturing and reflecting market sentiment, thereby yielding valuable insights into financial markets. This method not only improves the accuracy and relevance of summaries but also ensures that their content is emotionally consistent with the source news, offering a new perspective on financial news summarization.

Suggested Citation

  • Shangyang Mou & Qiang Xue & Xunquan Chen & Jinhui Chen & Ryoichi Takashima & Tetsuya Takiguchi & Yasuo Ariki, 2025. "Prefix tuning with prompt augmentation for efficient financial news summarization," Journal of Computational Social Science, Springer, vol. 8(1), pages 1-16, February.
    • Handle: RePEc:spr:jcsosc:v:8:y:2025:i:1:d:10.1007_s42001-024-00352-w
    DOI: 10.1007/s42001-024-00352-w
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s42001-024-00352-w
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s42001-024-00352-w?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Pekka Malo & Ankur Sinha & Pekka Korhonen & Jyrki Wallenius & Pyry Takala, 2014. "Good debt or bad debt: Detecting semantic orientations in economic texts," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 65(4), pages 782-796, April.
    2. Xingchen Wan & Jie Yang & Slavi Marinov & Jan-Peter Calliess & Stefan Zohren & Xiaowen Dong, 2020. "Sentiment Correlation in Financial News Networks and Associated Market Movements," Papers 2011.06430, arXiv.org, revised Feb 2021.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Costola, Michele & Hinz, Oliver & Nofer, Michael & Pelizzon, Loriana, 2023. "Machine learning sentiment analysis, COVID-19 news and stock market reactions," Research in International Business and Finance, Elsevier, vol. 64(C).
    2. Fabian Billert & Stefan Conrad, 2024. "A Framework for the Construction of a Sentiment-Driven Performance Index: The Case of DAX40," Papers 2409.20397, arXiv.org.
    3. Kirtac, Kemal & Germano, Guido, 2024. "Sentiment trading with large language models," Finance Research Letters, Elsevier, vol. 62(PB).
    4. Chen, Cathy Yi-Hsuan & Fengler, Matthias R. & Härdle, Wolfgang Karl & Liu, Yanchu, 2022. "Media-expressed tone, option characteristics, and stock return predictability," Journal of Economic Dynamics and Control, Elsevier, vol. 134(C).
    5. Gianluca Anese & Marco Corazza & Michele Costola & Loriana Pelizzon, 2023. "Impact of public news sentiment on stock market index return and volatility," Computational Management Science, Springer, vol. 20(1), pages 1-36, December.
    6. Fengbin Zhu & Junfeng Li & Liangming Pan & Wenjie Wang & Fuli Feng & Chao Wang & Huanbo Luan & Tat-Seng Chua, 2025. "Towards Temporal-Aware Multi-Modal Retrieval Augmented Generation in Finance," Papers 2503.05185, arXiv.org, revised Aug 2025.
    7. Paola Cerchiello & Giancarlo Nicola, 2018. "Assessing News Contagion in Finance," Econometrics, MDPI, vol. 6(1), pages 1-19, February.
    8. Travis Adams & Andrea Ajello & Diego Silva & Francisco Vazquez-Grande, 2023. "More than Words: Twitter Chatter and Financial Market Sentiment," Papers 2305.16164, arXiv.org.
    9. Chandan Singh & Armin Askari & Rich Caruana & Jianfeng Gao, 2023. "Augmenting interpretable models with large language models during training," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    10. Dolaeva, Aishat & Beliaeva, Uliana & Grigoriev, Dmitry & Semenov, Alexander & Rysz, Maciej, 2025. "Analyzing and forecasting P/E ratios using investor sentiment in panel data regression and LSTM models," International Review of Economics & Finance, Elsevier, vol. 98(C).
    11. Xiao-Yang Liu & Guoxuan Wang & Hongyang Yang & Daochen Zha, 2023. "FinGPT: Democratizing Internet-scale Data for Financial Large Language Models," Papers 2307.10485, arXiv.org, revised Nov 2023.
    12. Borchert, Philipp & Coussement, Kristof & De Weerdt, Jochen & De Caigny, Arno, 2024. "Industry-sensitive language modeling for business," European Journal of Operational Research, Elsevier, vol. 315(2), pages 691-702.
    13. Martina Halouskov'a & v{S}tefan Ly'ocsa, 2025. "Forecasting U.S. equity market volatility with attention and sentiment to the economy," Papers 2503.19767, arXiv.org.
    14. Priyank Sonkiya & Vikas Bajpai & Anukriti Bansal, 2021. "Stock price prediction using BERT and GAN," Papers 2107.09055, arXiv.org.
    15. Duygu Ider & Stefan Lessmann, 2022. "Forecasting Cryptocurrency Returns from Sentiment Signals: An Analysis of BERT Classifiers and Weak Supervision," Papers 2204.05781, arXiv.org, revised Mar 2023.
    16. Moritz Scherrmann, 2023. "Multi-Label Topic Model for Financial Textual Data," Papers 2311.07598, arXiv.org.
    17. Darko B. Vuković & Senanu Dekpo-Adza & Stefana Matović, 2025. "AI integration in financial services: a systematic review of trends and regulatory challenges," Palgrave Communications, Palgrave Macmillan, vol. 12(1), pages 1-29, December.
    18. Ankur Sinha & Chaitanya Agarwal & Pekka Malo, 2025. "FinBloom: Knowledge Grounding Large Language Model with Real-time Financial Data," Papers 2502.18471, arXiv.org.
    19. Bledar Fazlija & Pedro Harder, 2022. "Using Financial News Sentiment for Stock Price Direction Prediction," Mathematics, MDPI, vol. 10(13), pages 1-20, June.
    20. David M. Goldberg & Nohel Zaman & Arin Brahma & Mariano Aloiso, 2022. "Are mortgage loan closing delay risks predictable? A predictive analysis using text mining on discussion threads," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 73(3), pages 419-437, March.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:jcsosc:v:8:y:2025:i:1:d:10.1007_s42001-024-00352-w. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.