Headline Generation for Stock Price Fluctuation Articles (2023)

Abstract: The purpose of this paper is to construct a model for the generation of sophisticated headlines pertaining to stock price fluctuation articles, derived from the articles’ content. With respect to this headline generation objective, this paper solves three distinct tasks: in addition to the task of generating article headlines, two other tasks of extracting security names, and ascertaining the trajectory of stock prices, whether they are rising or declining. Regarding the headline generation task, we also revise the task as the model utilizes the outcomes of the security name extraction and rise/decline determination tasks, thereby for the purpose of preventing the inclusion of erroneous security names. We employed state-of-the-art pre-trained models from the field of natural language processing, fine-tuning these models for each task to enhance their precision. The dataset utilized for fine-tuning comprises a collection of articles delineating the rise and decline of stock prices. Consequently, we achieved remarkably high accuracy in the dual tasks of security name extraction and stock price rise or decline determination. For the headline generation task, a significant portion of the test data yielded fitting headlines.

## introduction for individuals engaged in stock trading, acquiring up-to-date information regarding stock price fluctuations is highly important. knowledge of not only whether stock prices have risen or declined, but also the underlying causes such as product launches or sociopolitical conditions, can inform future investment strategies. while news articles on stock trading serve as a primary source of information, manually creating articles for a diverse array of securities foot_0 is considered challenging due to time constraints. hence, it is desirable to construct a system capable of automatically generating stock price fluctuation articles using quantitative stock information and related textual data. articles typically consist of a body and a headline, with the latter expected to succinctly include, at a minimum, the security name of the fluctuating stock and a term indicating whether the stock price has risen or declined. this paper assumes that the body of a stock price fluctuation article has already been generated automatically and aims to develop a headline generation model that produces the headline based on the article's content (figure 1 ) @xcite . with respect to the headline generation model, which takes the article's content as input, we solve three distinct tasks: in addition to the task of generating article headlines, two other tasks of extracting the relevant security names, and determining stock price rise or decline. regarding the headline generation task, we also revise the task as the model utilizes the outcomes of the security name extraction and rise/decline determination tasks, thereby for the purpose of preventing the inclusion of erroneous security names. in each task, we employ pre-trained models that have demonstrated high performance in the field of natural language processing. by fine-tuning foot_2 these pre-trained models for each respective task, we aim to enhance the models' accuracy. the dataset used for fine-tuning consists of the stock price fluctuation article dataset, which is composed of articles regarding the rise and decline of stock prices. in this paper, we report the results of the three tasks above as well as the preliminary evaluation results of the revised task of the headline generation task. our contributions are as follows: ## related work in the realm of research related to generating headlines from news article content, there exist preced-ing related work (rush et al., 2015; chopra et al., 2016; nallapati et al., 2016; kikuchi et al., 2016; takase and okazaki, 2019; hitomi et al., 2019). among them, rush et al. (2015) developed the first approach to neural abstractive summarization. after that, chopra et al. (2016) used the encoderdecoder framework, and nallapati et al. (2016) incorporated additional features such as parts-ofspeech tags and named entities. there also exist attempts to control the output length in neural abstractive summarization (kikuchi et al., 2016; takase and okazaki, 2019; hitomi et al., 2019). for example, in the works of hitomi et al. (2019), they propose a corpus for evaluating headline generation models that take output length into consideration. in this paper, on the other hand, we focus on stock-specific terminology characteristic of stock-related news articles, striving to create headlines more suitable for stock price fluctuation articles. additionally, in the context of studies on news article headlines and stock prices, there exist several prior investigation. the proposed methods for stock price prediction using news headlines vary across different approaches. in one approach, it is suggested to combine news headlines with technical indicators to predict stock prices (kalshani et al., 2020). another approach is also proposed, which predicts the short-term movement of stock prices after financial news events using only the headlines of the news (chen, 2021). in a third approach, they discussed the failure of the efficient market hypothesis and proposed a project on stock trend prediction using news (kalyani et al., 2016). two other approaches evaluate different machine learning and deep learning methods, such as support vector machines (svm) and long short-term memory (lstm), to predict stock price movement using financial news (liu et al., 2018; gong et al., 2021). ## revised task of headline generation and its preliminary evaluation results in the headline generation model described in the previous section, high accuracy was achieved for the tasks of determining whether the stock price rose or declined and extracting the security name. however, for the headline generation task, the model generated titles containing incorrect information not found in the input context for 20 out of 100 test examples. here, we focus on the fact that six of these examples had incorrect security names and aim to improve the headline generation task by inserting the security name and a tag ("r" or "d") representing whether the stock price rose or declined in the input context 2930 to prevent headlines with incorrect security names from being generated. for fine-tuning the pre-trained model for the revised headline generation task, we used the huggingface transformers summarization library, as in the case of the headline generation task of section 4.4. the training data of the stock price fluctuation article dataset and the various parameters during training were also set to the same values as the previous headline generation task. we inputted the pair of the article headline and the body text with the security name and rise/decline tags added to the model. after fine-tuning, we inputted the 200 test examples from the stock price fluctuation article dataset to the model and performed the same performance test as in the previous headline generation task. as shown in the row of "after task revision" in sg は続伸、創業 30 周 年 記 念 優 待 制度の内容を継続 へ (= sg continues to rise, continues 30th anniversary commemorative benefits program) sg は続伸、株主優待制度の 変更を好感 (= sg continues to rise, favorably received changes to shareholder benefits program) (information corresponding to the content of the article is included in the model-generated headline, which is comparable to the reference headline. ) m 社が大幅続伸している。きょう付の日本経済新聞朝刊で、 「1 千 キロメートル離れた場所から複数のドローンをまとめて操作できる システムを 2021 年度にも実用化する」と報じられており、これが 好材料視されているようだ。. . . (= m company has made significant gains. according to today's morning edition of the nikkei shimbun, it was reported that they "will commercialize a system that can control multiple drones from a distance of 1,000 kilometers by the end of fiscal 2021," which seems to be viewed as good news. . . .) m 社が大幅続伸、 1000 キ ロ 先 の ド ローン操作する技 術を 21 年度にも 実用化と報じられ る (= m company surges, reported to commercialize 1000km distant drone control technology by fy 2021) m 社が大幅続伸、1 千キロ メートル離れた場所から複数 のドローンをまとめて操作 (= m company surges, control multiple drones from a distance of 1,000 kilometers) (information corresponding to the content of the article is included in the model-generated headline, while the modelgenerated headline is not sufficient) k 社が一時ストップ高まで買われた。同社はきょう、画像処理検査エ ンジンの販売強化などを目的に 12 月 1 日から社長直轄のプロジェク トチームでの活動を開始すると発表しており、今後の展開などが期 待されているようだ。同社はこれまでに培った技術やノウハウを生 かし、さまざまな顧客ニーズに応えられる画像処理外観検査用のエ ンジンを開発し、従来の液晶向け以外の分野にも進出・拡販すること で、収益の拡大と安定によって、収益性の高い事業体制を確立する としている。(= k company's stock temporarily hit the daily limit high. the company announced today that it will start a project team under the direct control of the president from december 1st, aiming to strengthen the sales of image processing inspection engines, among other goals. it seems that future developments are expected. the company plans to establish a highly profitable business structure by expanding and stabilizing revenues, by developing image processing exterior inspection engines that can respond to various customer needs using the technology and know-how they have cultivated so far, and expanding into fields other than traditional liquid crystal displays.) company temporarily hits daily limit high, activities to strengthen sales of image processing inspection engines begin) k 社が一時 s 高、画像処理 検査エンジンの販売強化など を目的に事業展開 (= k company temporarily hits s high, business expansion aimed at strengthening sales of image processing inspection engines) according to the morning edition of the nihon keizai shimbun on the 23rd, "one-third of the world's airplanes, or 8,600 aircraft, are still grounded even in august, the vacation season. the leasing industry and financial products that have invested based on demand expansion are exposed to risks, and new aircraft are being canceled one after another." it seems that vigilance is increasing towards the company, which mainly deals with aircraft leasing. . . .) after the close of trading on the 12th, the company announced its second quarter consolidated financial results (from april to september), with net sales of 2.093 billion yen (an increase of 8.8% year-on-year), operating profit of 393 million yen (an increase of 22.0% year-on-year), and net profit of 254 million yen (an increase of 7.7% year-on-year). the record-breaking operating profit and net profit in the second quarter are well-received.. . . rouge-2 (recall) was 36.94%, and rouge-l (recall) was 52.27%. next, we manually evaluated the 100 headlines generated for the test data, which were previously evaluated in the headline generation task of section 4.4. upon examining whether the information in the generated headlines corresponded to the information in the input context, we found that 82 out of 100 examples contained relevant information. out of these 82 examples, 30 (36.6%) had exact matches in terms of character strings, and 62 (75.6%) were considered appropriate headlines. in addition, while all the 100 examples contained correct information about stock price rise and decline, 5 cases had errors in the security names. ## conclusion in this paper, we fine-tuned the pre-trained models xlm-roberta and mt5 using the stock price fluctuation article dataset to perform three tasks: generating article headlines, extracting target security names, and determining stock price rise and decline. we constructed a stock price fluctuation article headline generation model. we were able to achieve quite high accuracy in the tasks of security name extraction and determining stock price rise and decline. for the headline generation task, appropriate headlines were generated for many test data, comparable to the actual article headlines. however, some generated headlines contained incorrect information, such as the target security names. we revised the headline generation task to prevent headlines containing incorrect security names from being generated. although we were able to reduce the number of headlines generated with incorrect security names while maintaining similar rouge (recall) scores as before the revision, the improvement was not significant. as future work, we would like to investigate methods such as setting loss functions for each of the tasks of security name extraction, stock price rise and decline determination, and headline generation, and performing multi-task learning to ensure that the security name information in the headlines is correct. this is to clarify whether the multi-task learning approach can improve the performance of the method employed in this paper, which is quite specific to the financial domain compared with the general language models studied in radford et al. (2019). another future work is to apply large language models (llms) such as chatgpt (openai, 2023) and llama (touvron et al., 2023) to the task studied in this paper.