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Although the increasing data density in recent years
positively affects our learning and development speed, it also
brings complexity and difficulties in decision-making processes.
The time allocated to interpret and understand all the data turns
into a process that users do not want. Especially in areas such as
the media sector, many news, video and audio content produced
cannot be used because of their length. In financial markets, a lot
of content is produced on the same subject throughout the day
due to foreign policy and politics. Markets are affected by a wide
range of news such as corporate earnings reports, mergers and
acquisitions, regulatory changes and geopolitical events. It
becomes a very difficult process for investors to follow these
news. At this point, automatic text and voice summarization
stands out as a powerful tool for processing complex information
quickly and effectively.
This paper deals with a specially created dataset for
summarizing Turkish stock market news and the automatic
summarization process with language models adapted to this
dataset. The Turkish language poses special challenges to natural
language processing models due to its unique agglutinative
structure and limited data resources. Compared to classical
natural language processing approaches, open source language
models that have emerged in recent years have achieved
significant success in different languages by making inferences
from big data. In this context, mt5-base, mbart-50, Gemini and
GPT 3.5 were used in our study. As a dataset, 240 news texts
were obtained from Dunya.com. As the second dataset, 7000
Turkish economy news texts from the Hugging-Face platform
were used. The two datasets were used for fine tuning approach
and comparison of the models. The datasets were divided into
70% training, 10% validation and 20% testing. The models were
run with a learning rate of 2e-5 and a number of cycles ranging
from 20 to 100. Rouge, Bleu and BertScore metrics were used in
the evaluation phase. The summarization models in the study
were converted into APIs. The models published as open source
on the Github platform can be used in various interfaces using
API addresses.
International Conference on Advanced Technologies, Computer Engineering and Science
ICATCES
Güncel SARIMAN
Nebi Berke İÇÖZ