Interactive Dialogue Interface for Personalized News Article Comprehension (2024)

Abstract: We developed an interface to explain news articles through dialogue by considering the user’s comprehension level. The interface generates several pertinent questions based on the ongoing dialogue and news article, and users advance the conversation by selecting a question. Based on the user’s selected questions, the interface estimates their comprehension level of the news article and adjusts the difficulty of the generated questions accordingly. This enables a personalized dialogue tailored to each user’s comprehension needs. The results of the baseline comparison experiments confirmed the usefulness of the interface.

## introduction organization for economic co-operation and development reported that 21% of individuals in the united states aged 16 years and over have low english literacy. this limitation results in them having difficulty reading longer texts such as news articles @xcite . in this paper, we construct an interactive news commentary dialogue interface that considers the user's comprehension level to increase the understandability of news articles. we constructed an interface, shown in figure 1 , that interactively explains news articles. the interface generates several pertinent questions based on the ongoing dialogue and news article, and users advance the conversation by selecting a question. this interactive approach is expected to provide users the opportunity to organize information and deepen their understanding. when users select a question, the system predicts their comprehension level of the news article based on the selected question and adjusts the difficulty of the next question accordingly. this feature allows the dialogue to be tailored to each user's knowledge level. title : brightest-ever cosmic explosion solved but new mysteries sparked system user system candidate questions level 1 question level 2 question level 2 question ## related works media often use question-and-answer series about news stories because of their clarity, but reporters manually create them; thus, the process is inefficient due to high costs and time requirements. @xcite proposed a method to automatically build chatbots from news articles. in these chatbots, the system presents the user with candidate questions. podcasts with automatic construction containing qa sessions on news were also analyzed, confirming the effectiveness of a conversational format @xcite . however, these questions are not customized to individual user characteristics, and the tailored dialogues are not generated for individual users. user characteristics, including their social group, influence the types of questions asked. @xcite introduced a method for producing ques-tions that consider these characteristics, training a text generation model on social media data that accounts for social groups and their expertise in specific domains. @xcite developed a conversational agent prototype that incorporates a strategy to account for the user's knowledge and ignorance regarding speech, validating the importance of considering user knowledge. drawing inspiration from these methodologies, we generate questions that incorporate the user's comprehension level of the news story. ## system overview we constructed an interactive interface to explain the contents of news articles, illustrated in figure 1 . the interface first presents the user with a brief introduction to the news article and three candidate questions. the user then selects one of these questions, and the system explains the article's content by answering the selected question. this approach reduces the user's burden by eliminating the need to think about questions independently. the purpose of this interface is to facilitate understanding by providing explanations tailored to individual users. candidate questions should reflect those the user might want to ask, considering the user's comprehension level. therefore, we generate and present candidate questions, considering the user's comprehension level. the system comprises three modules, as shown in figure 2 : an introduction generation module, a candidate question generation module, and an answer generation module. each module uses @xcite as the large language model. ## experiment to evaluate the usefulness of this interface in reading news articles, we conducted a subject exper-iment to compare it with three different baseline settings. crowdworker read a randomly assigned article in a randomly assigned setting and completed a comprehension test and questionnaire. ## conclusion we developed a news commentary dialogue interface that considers the user's comprehension level. the interface alleviates the user's burden by pre-senting multiple automatically generated question candidates and having the system answer the selected questions. it aims to tailor the dialogue to each user by predicting their comprehension level of the news content on the basis of the selected questions and presenting question candidates that account for this level of understanding. in our experiment, we implemented the proposed interface and quantitatively evaluated its effectiveness by using crowd-sourcing. comparison experiments between the proposed interface and baselines confirmed that the proposed interface enhances users' reading comprehension. in the open-ended responses to the questionnaire during the experiment, one participant said, "conveying the content of news solely through text is difficult." based on this feedback, our further research will consider incorporating images along with text.