Generating Multiple Questions from Presentation Transcripts: A Pilot Study on Earnings Conference Calls (2023)

Abstract: In various scenarios, such as conference oral presentations, company managers’ talks, and politicians’ speeches, individuals often contemplate the potential questions that may arise from their presentations. This common practice prompts the research question addressed in this study: to what extent can models generate multiple questions based on a given presentation transcript? To investigate this, we conduct pilot explorations using earnings conference call transcripts, which serve as regular meetings between professional investors and company managers. We experiment with different task settings and methods and evaluate the results from various perspectives. Our findings highlight that incorporating key points retrieval techniques enhances the accuracy and diversity of the generated questions.

## introduction preparing for an oral presentation is a common task in various domains, particularly in professional settings. for instance, researchers who have had their papers accepted at conferences need to deliver either an oral or poster presentation to share their findings with fellow researchers. politicians must prepare for debates during election periods, while company managers are required to deliver speeches to update investors on company operations. when crafting their presentation drafts, a fundamental concern arises: what kinds of questions might the audience ask? this paper introduces a novel task, multi-question generation (mqg), to assist presenters in preparing for q&a sessions. diverging from previous studies that predominantly focused on one-to-one question generation tasks @xcite , the proposed mqg task is a one-to-many question generation task. in other words, after the presentation, audiences typically pose multiple questions. to investigate these research questions, we collect earnings conference call transcriptions, regular meetings between company managers and professional analysts. our aim with the proposed mqg task is to generate questions similar to those posed by analysts after listening to managers' presentations. we provide the collected dataset for future research endeavors. to address these research questions, we propose the mqg-kr approach, combining mqg with keypoint retriever (kr). keypoints are retrieved using bert, enhancing question generation performance. we explore two task settings: generating all questions simultaneously and generating questions sequentially. preliminary evaluations show that the mqg-kr approach improves the diversity of generated questions. ## related work generating good questions is a challenging task for both humans and machines. previous studies have primarily focused on one-to-one question generation, often centered around generating questions for reading comprehension tests. heilman and smith @xcite introduced syntactic transformations to convert given statement sentences into questions. jia et al. @xcite improved performance by incorporating a paraphrase module into their model. wang et al. @xcite generated questions based on the knowledge graph path of the input sentence. song et al. @xcite matched given answers and paragraphs to augment context information for question generation. in contrast to previous studies focusing on generating questions for machine reading comprehension tests, our work proposes generating questions in live presentations. there are distinct differences between these tasks. questions for machine reading comprehension tests inquire about content, with most answers explicitly provided within the text. complex questions may require some common sense inference. however, professional analysts posing questions during presentations will not ask for information already provided; instead, they seek clarification or further explanation @xcite . consequently, models generating such questions must first identify unclear or insufficiently detailed portions of the presentation. although earnings conference calls have been widely studied for various tasks such as stock movement prediction @xcite , volatility forecasting @xcite , and summarization @xcite , the question generation task has received little attention. thus, our paper aims to fill this research gap by providing an initial exploration of question generation from earnings conference calls. ## method approach comparison is illustrated in figure 1 , highlighting two key distinctions between the proposed mqg-kr and previous approaches. firstly, instead of providing the answer (condition) as input to the models, the proposed approach identifies keypoints that are likely to prompt questions from the audience and generates questions based on these keypoints. secondly, while previous studies primarily focused on generating a single question, the proposed mqg-kr aims to generate multiple questions. in this section, we present two modules within the proposed mqg-kr framework: the keypoint retriever and the question generator. ## conclusion this paper introduces the novel task of mqg and explores its potential applications in generating multiple questions based on a given presentation transcript. our experiments demonstrate that incorporating a keypoint retriever improves the accuracy and diversity of the generated questions. this research contributes to the fields of nlp and information retrieval (ir) by offering insights into the mqg task and its relevance in various scenarios. our findings provide a valuable starting point for future research in mqg. by better anticipating and preparing for question and answer sessions, presenters can benefit from the generated questions. we believe that this work stimulates further discussions, advancements, and collaborations in the exciting field of multi-question generation, driving the development of more effective and efficient question generation models.