$\texttt{M$^3$FinMeeting}$: A Multilingual, Multi-Sector, and Multi-Task Financial Meeting Understanding Evaluation Dataset
Authors: Jie Zhu, Junhui Li, yalong wen, Xiandong Li, Lifan Guo, Feng Chen
Accepted at: acl25

M^3FinMeeting: A Multilingual, Multi-Sector, and Multi-Task Financial Meeting Understanding Evaluation Dataset

Jie Zhu1,2, Junhui Li1*, Yalong Wen2, Xiandong Li3, Lifan Guo2, Feng Chen2
1School of Computer Science and Technology, Soochow University
2Qwen DianJin Team, Alibaba Cloud Computing
3Nanjing University
zhujie951121@gmail.com, lijunhui@suda.edu.cn
{lifan.lg, betterman.chenf}@alibaba-inc.com

Abstract
Recent breakthroughs in large language models (LLMs) have led to the development of new benchmarks for evaluating their performance in the financial domain. However, current financial benchmarks often rely on news articles, earnings reports, or announcements, making it challenging to capture the real-world dynamics of financial meetings. To address this gap, we propose a novel benchmark called M^3FinMeeting, which is a multilingual, multi-sector, and multi-task dataset designed for financial meeting understanding. First, M^3FinMeeting supports English, Chinese, and Japanese, enhancing comprehension of financial discussions in diverse linguistic contexts. Second, it encompasses various industry sectors defined by the Global Industry Classification Standard (GICS), ensuring that the benchmark spans a broad range of financial activities. Finally, M^3FinMeeting includes three tasks: summarization, question-answer (QA) pair extraction, and question answering, facilitating a more realistic and comprehensive evaluation of understanding. Experimental results with seven popular LLMs reveal that even the most advanced long-context models have significant room for improvement, demonstrating the effectiveness of M^3FinMeeting as a benchmark for assessing LLMs’ financial meeting comprehension skills.1

1 Introduction
Financial meetings, whether in person or virtual, serve as critical venues for decision-making, negotiation, and strategy formulation among various participants. Although large language models (LLMs) have demonstrated impressive performance across multiple natural language processing (NLP) tasks (OpenAI, 2023), it remains uncertain how they can effectively understand and process lengthy speech texts to help financial professionals expedite their work. Key capabilities, such as summarizing crucial points, responding to inquiries, and extracting question-answer pairs, are particularly beneficial for enhancing productivity and facilitating informed discussions in this context.

In the financial domain, there are various benchmarks available in different languages, such as FinQA (Chen et al., 2021) and ConvFinQA (Chen et al., 2022) in English, as well as CFLUE (Zhu et al., 2024) and the CCKS series shared tasks in Chinese (Tianchi, 2019, 2020, 2021, 2022). However, these datasets are primarily sourced from financial news and earnings reports, lacking content from real-world financial meetings. Additionally, they are monolingual, limited to English or Chinese. To address this gap, we introduce a new dataset called M^3FinMeeting, designed for multilingual and multi-sector evaluation of financial meeting understanding, featuring multiple tasks. First, M^3FinMeeting supports multiple languages, including English, Chinese and Japanese, enhancing the understanding of financial discussions across different linguistic contexts. Second, it encompasses all 11 industry sectors defined by Global Industry Classification Standard (GICS). Finally, M^3FinMeeting includes multiple classical NLP tasks such as summarization, question answering, and question-answer pair extraction. Importantly, since financial meetings typically last one to two hours, M^3FinMeeting provides long-context data, which is essential for assessing the ability of LLMs to handle complex tasks. Additionally, the tasks within M^3FinMeeting require LLMs to generate long responses, allowing for a thorough assessment of their capabilities in producing coherent and relevant outputs in challenging scenarios.

Based on M^3FinMeeting, we assess the effectiveness of seven representative LLMs including two OpenAI GPTs and five open-sourced LLMs. The experimental results indicate that Qwen2.5-72B-
Instruct (Yang et al., 2024b) significantly outperforms other large language models (LLMs), achieving overall scores above 70 when evaluated by GPT-4 (OpenAI, 2023). However, this also suggests that even the most advanced LLMs currently available struggle with the tasks in M^3FinMeeting, revealing substantial rooms for performance improvement.

Our main contributions can be summarized as follows:

• M^3FinMeeting introduces a novel evaluation benchmark specifically designed for financial meetings, addressing the lack of real-world financial meeting data in existing benchmarks.

• M^3FinMeeting supports multilingual evaluation in English, Chinese, and Japanese, spans the 11 industry sectors defined by GICS, and includes three key NLP tasks: summarization, question-answer pair extraction, and question answering. All documents are carefully annotated by financial analysts to ensure high-quality and accurate evaluation.

• Through extensive experiments and detailed analyses, we thoroughly assess the performance of state-of-the-art long-context LLMs, offering valuable insights into their limitations and potential improvements for better long-context modeling in financial scenarios.

2 Related Work

2.1 Financial Evaluation Benchmarks

Financial NLP has become a key application area for LLMs, attracting increasing attention for its benchmarks. Table 1 summarizes recent benchmarks in this field. In English, FINQA by Chen et al. (2021) contains 8,281 question-answering pairs, with their numerical reasoning, from the earnings reports of S&P 500 companies. ECTSum by (Mukherjee et al., 2022) contains 2,425 transcripts of earnings calls, paired with short telegram-style bullet point summaries. FLUE by (Shah et al., 2022) and FLARE by Xie et al. (2024) offer heterogeneous benchmarks with various financial NLP tasks from existing datasets, including financial sentiment detection (Malo et al., 2014), named entity recognition (Alvarado et al., 2015), news headline classification (Sinha and Khandait, 2020), question answering (Maia et al., 2018), boundary detection tasks (Au et al., 2021), text summarization (Zhou et al., 2021; Mukherjee et al., 2022), and stock movement prediction (Xu and Cohen, 2018; Wu et al., 2018; Soun et al., 2022). FinTextQA by Chen et al. (2024) includes 1,262 long-form QA pairs from finance textbooks and government websites. BizBench by Krumdick et al. (2024) features eight quantitative reasoning tasks from professional exams, earnings reports, and other financial sources. FINANCEBENCH by Islam et al. (2023) includes 10,231 question-answer-evidence triplets from public filings and earnings reports.

In Chinese, the CCKS series has released several datasets specifically designed for various event extraction tasks (Tianchi, 2019, 2020, 2021, 2022). Additionally, significant effort has gone into creating evaluation datasets for document-level extraction from financial announcements, judgments, and news articles. These include DCFEE (Yang et al., 2018), DuEE-Fin (Han et al., 2022), Doc2EDAG (Zheng et al., 2019), and CFinDEE (Zhang et al., 2024a). Moreover, both BBT-CFLEB dataset (Lu et al., 2023) and CFLUE dataset (Zhu et al., 2024) serve as heterogeneous benchmarks that cover a wide range of NLP tasks, including multiple-choice question answering and reasoning, news and text classification, summarization, relation extraction, sentiment classification, question answering, and reading comprehension.

In addition to the benchmarks mentioned above, several datasets have been developed in the financial domain to support the training of financial LLMs, such as FLANG (Shah et al., 2022), Pixiu (Xie et al., 2024), InvestLM (Yang et al., 2023b), FinGPT (Yang et al., 2023a), and DianJinR1 (Zhu et al., 2025). However, it is important to note that most of these datasets primarily rely on sources such as financial news, announcements, filings, and earnings reports. In contrast, our focus is on financial meetings, which provide unique insights and discussions that are often absent from existing benchmarks.

2.2 Summarization and Question-Answering Benchmarks

Summarization Benchmarks. Recent studies (Goyal et al., 2022; Zhang et al., 2024b; Pu et al., 2023) indicate that human preferences strongly favor summaries produced by LLMs over those generated by fine-tuned models or even reference summaries. This highlights the need to create new datasets that can comprehensively evaluate the summarization abilities of LLMs.
<table>
  <tr>
    <th>Language</th>
    <th>Dataset</th>
    <th>Sources</th>
    <th>Tasks</th>
  </tr>
  <tr>
    <td rowspan="7">English</td>
    <td>FinQA (<a href="https://arxiv.org/abs/2106.09435">Chen et al., 2021</a>)</td>
    <td>Earnings reports</td>
    <td>Question answering & reasoning</td>
  </tr>
  <tr>
    <td>ConvFinQA (<a href="https://arxiv.org/abs/2206.08174">Chen et al., 2022</a>)</td>
    <td>Reports</td>
    <td>Question answering & reasoning</td>
  </tr>
  <tr>
    <td>FLUE (<a href="https://arxiv.org/abs/2206.08174">Shah et al., 2022</a>)</td>
    <td>Multiple</td>
    <td>5 tasks</td>
  </tr>
  <tr>
    <td>ECTSum (<a href="https://arxiv.org/abs/2206.08174">Mukherjee et al., 2022</a>)</td>
    <td>Call transcripts</td>
    <td>Summarization</td>
  </tr>
  <tr>
    <td>FLARE (<a href="https://arxiv.org/abs/2401.04824">Xie et al., 2024</a>)</td>
    <td>Multiple</td>
    <td>8 tasks</td>
  </tr>
  <tr>
    <td>FinTextQA (<a href="https://arxiv.org/abs/2401.04824">Chen et al., 2024</a>)</td>
    <td>Long reports</td>
    <td>Question answering</td>
  </tr>
  <tr>
    <td>BizBench (<a href="https://arxiv.org/abs/2401.04824">Krumdick et al., 2024</a>)</td>
    <td>Multiple</td>
    <td>8 quantitative reasoning tasks</td>
  </tr>
  <tr>
    <td>FinanceBench (<a href="https://arxiv.org/abs/2309.13477">Islam et al., 2023</a>)</td>
    <td>Public filings, Earnings reports</td>
    <td>Question answering</td>
  </tr>
  <tr>
    <td rowspan="7">Chinese</td>
    <td>CCKS Tianchi (2019, 2020, 2021, 2022)</td>
    <td>Multiple</td>
    <td>Multiple event-related tasks</td>
  </tr>
  <tr>
    <td>DCFEE (<a href="https://arxiv.org/abs/1806.07227">Yang et al., 2018</a>)</td>
    <td>Announcements</td>
    <td>Document-level event extraction</td>
  </tr>
  <tr>
    <td>Doc2EDAG (<a href="https://arxiv.org/abs/1906.07227">Zheng et al., 2019</a>)</td>
    <td>Announcements</td>
    <td>Document-level event extraction</td>
  </tr>
  <tr>
    <td>DuEE-Fin (<a href="https://arxiv.org/abs/2206.08174">Han et al., 2022</a>)</td>
    <td>Multiple</td>
    <td>Document-level event extraction</td>
  </tr>
  <tr>
    <td>CFinDEE (<a href="https://arxiv.org/abs/2401.04824">Zhang et al., 2024a</a>)</td>
    <td>News articles</td>
    <td>Document-level event extraction</td>
  </tr>
  <tr>
    <td>BBT-CFLEB (<a href="https://arxiv.org/abs/2309.13477">Lu et al., 2023</a>)</td>
    <td>Multiple</td>
    <td>6 tasks</td>
  </tr>
  <tr>
    <td>CFLUE (<a href="https://arxiv.org/abs/2401.04824">Zhu et al., 2024</a>)</td>
    <td>Multiple</td>
    <td>Multiple-choice Question Answering & Reasoning and 5 tasks</td>
  </tr>
  <tr>
    <td>English, Chinese, Japanese</td>
    <td>M^3FinMeeting (Ours)</td>
    <td>Meetings</td>
    <td>Summarization, Question answering, QA pair extraction</td>
  </tr>
</table>

Table 1: Summary of recent financial benchmarks.

For instance, SumSurvey by <a href="https://arxiv.org/abs/2401.04824">Liu et al. (2024a)</a> is a new dataset focused on summarizing long scientific survey papers. REFINESUMM by <a href="https://arxiv.org/abs/2401.04824">Patil et al. (2024)</a> is tailored for image-text multimodal summarization. MovieSum by <a href="https://arxiv.org/abs/2401.04824">Saxena and Keller (2024)</a> comprises 2,200 pairs of movie screenplays and their corresponding summaries. Additionally, HeSum by <a href="https://arxiv.org/abs/2401.04824">Paz-Argaman et al. (2024)</a> is a novel benchmark dataset specifically designed for the low-resource Hebrew language. Our research also emphasizes long context, but we concentrate on meetings as our primary source, allowing us to analyze communication dynamics within this particular area. Several meeting speech summarization benchmarks are available, such as AMI (<a href="https://arxiv.org/abs/0506007">Carletta et al., 2005</a>) and ICSI (<a href="https://arxiv.org/abs/0306007">Janin et al., 2003</a>), which consist of English-language video (or audio) recordings of meetings, typically spanning tens of hours.

<b>Question-Answering Benchmarks.</b> Question Answering (QA) is a key task in NLP, with increasingly challenging benchmarks being developed, including those focused on the financial domain. For example, several benchmarks have been introduced to assess the long context understanding capabilities of LLMs (<a href="https://arxiv.org/abs/2401.04824">Li et al., 2024; Wang et al., 2024; Bai et al., 2024b</a>). Additionally, many multimodal QA benchmarks have emerged to assess LLMs’ capabilities in detecting and retrieving relevant information from various inputs, such as images, videos, texts, tables, and meetings (<a href="https://arxiv.org/abs/2309.13477">Li et al., 2023; Fu et al., 2023; Jin et al., 2024; Prasad et al., 2023</a>).

<b>3 M^3FinMeeting: A Financial Meeting Benchmark</b>

<b>3.1 Overview</b>

A formal financial meeting typically involves a few participants and lasts one to two hours. It promotes discussions among key participants, allowing for decision-making and strategic planning based on real-time information and reports. Participants express their opinions verbally, which distinguishes both the content and style of financial meetings from financial news, earnings reports, or announcements. The M^3FinMeeting benchmark, based on hundreds real financial meetings, includes three NLP tasks: summarization, question answering, and QA pair extraction. To reflect real-world sce-
<table>
  <tr>
    <th colspan="4">Overview</th>
  </tr>
  <tr>
    <th>Language</th>
    <th>#Meeting</th>
    <th>Avg hour</th>
    <th>Avg token</th>
  </tr>
  <tr>
    <td>EN</td>
    <td>100</td>
    <td>0.96</td>
    <td>10,086</td>
  </tr>
  <tr>
    <td>ZH</td>
    <td>400</td>
    <td>1.15</td>
    <td>11,740</td>
  </tr>
  <tr>
    <td>JA</td>
    <td>100</td>
    <td>1.01</td>
    <td>13,284</td>
  </tr>
</table>

<table>
  <tr>
    <th colspan="4">GICS Sector</th>
  </tr>
  <tr>
    <th>Sector</th>
    <th>Language</th>
    <th>#Meeting</th>
    <th>Avg token</th>
  </tr>
  <tr>
    <td>Com. Services</td>
    <td>EN, ZH, JA</td>
    <td>36</td>
    <td>11,240</td>
  </tr>
  <tr>
    <td>Con. Dis.</td>
    <td>EN, ZH, JA</td>
    <td>122</td>
    <td>11,104</td>
  </tr>
  <tr>
    <td>Con. Staples</td>
    <td>EN, ZH, JA</td>
    <td>52</td>
    <td>11,699</td>
  </tr>
  <tr>
    <td>Energy</td>
    <td>EN, ZH, JA</td>
    <td>32</td>
    <td>15,841</td>
  </tr>
  <tr>
    <td>Financials</td>
    <td>EN, ZH, JA</td>
    <td>49</td>
    <td>12,835</td>
  </tr>
  <tr>
    <td>Healthcare</td>
    <td>EN, ZH, JA</td>
    <td>47</td>
    <td>13,937</td>
  </tr>
  <tr>
    <td>Industrials</td>
    <td>EN, ZH, JA</td>
    <td>111</td>
    <td>12,062</td>
  </tr>
  <tr>
    <td>IT</td>
    <td>EN, ZH, JA</td>
    <td>98</td>
    <td>13,430</td>
  </tr>
  <tr>
    <td>Materials</td>
    <td>EN, ZH, JA</td>
    <td>32</td>
    <td>11,393</td>
  </tr>
  <tr>
    <td>RealEstate</td>
    <td>EN, ZH, JA</td>
    <td>13</td>
    <td>15,270</td>
  </tr>
  <tr>
    <td>Utilities</td>
    <td>EN, ZH, JA</td>
    <td>8</td>
    <td>17,290</td>
  </tr>
</table>

<table>
  <tr>
    <th colspan="4">Length Set</th>
  </tr>
  <tr>
    <th>Length</th>
    <th>Language</th>
    <th>#Meeting</th>
    <th>Avg token</th>
  </tr>
  <tr>
    <td>Set1 (0-5K)</td>
    <td>EN, ZH, JA</td>
    <td>59</td>
    <td>3,546</td>
  </tr>
  <tr>
    <td>Set2 (5-10K)</td>
    <td>EN, ZH, JA</td>
    <td>164</td>
    <td>7,509</td>
  </tr>
  <tr>
    <td>Set3 (10-15K)</td>
    <td>EN, ZH, JA</td>
    <td>195</td>
    <td>12,476</td>
  </tr>
  <tr>
    <td>Set4 (15-20K)</td>
    <td>EN, ZH, JA</td>
    <td>124</td>
    <td>17,419</td>
  </tr>
  <tr>
    <td>Set5 (>20K)</td>
    <td>EN, ZH, JA</td>
    <td>58</td>
    <td>25,281</td>
  </tr>
</table>

Table 2: Data statistics of M^3FinMeeting benchmark.

<table>
  <tr>
    <th>Language</th>
    <th>AST</th>
    <th># SS</th>
    <th>C<sub>token</sub></th>
    <th>C<sub>sent</sub></th>
  </tr>
  <tr>
    <td>EN</td>
    <td>927</td>
    <td>9.20</td>
    <td>10.88</td>
    <td>10.49</td>
  </tr>
  <tr>
    <td>ZH</td>
    <td>2,524</td>
    <td>15.17</td>
    <td>4.65</td>
    <td>3.62</td>
  </tr>
  <tr>
    <td>JA</td>
    <td>1,149</td>
    <td>8.24</td>
    <td>11.56</td>
    <td>11.92</td>
  </tr>
</table>

Table 3: Statistics for the summarization task. Here, AST is for average summary token, #SS is for averaged number of section summary, C<sub>token</sub> and C<sub>sent</sub> for the compression ratio at token-level and sentence-level, respectively.

transcribed speech documents can be structured into sections based on discussion topics, with each section having its own summary, which we refer to section summary. We concatenate these section summaries sequentially to create a summary of the entire document. LLMs must implicitly identify and segment the document into various sections and then extract key point from each. Given transcribed speech documents and their reference summaries, we follow Koh et al. (2022) to compute the compression ratio of a source document length against its reference summary length at both token-level and sentence-level. As shown in Table 3, on average an English meeting contains 9.20 section summaries totaling 927 tokens.

3.2.2 QA Pair Extraction

The task of question-answer (QA) pair extraction involves identifying and extracting relevant QA pairs from transcribed financial meetings. This is crucial for analyzing discussions and making key insights readily accessible. To successfully perform this task, LLMs must recognize various types of questions posed during the meeting and accurately locate their corresponding answers. For example, questions like What were we just talking about? should be disregarded as they lack meaningful information. Additionally, participants may ask multiple questions at once, while responses may address them sequentially. This complexity requires LLMs to discern the structure of the dialogue, correctly pair each question with its answer. By employing natural language processing techniques, this task enhances information retrieval efficiency and facilitates subsequent analyses, providing structured and contextualized insights from the meeting. Table 4 shows statistics of QA pairs.

3.2.3 Question Answering

The question answering (QA) task evaluates the ability of LLMs to localize knowledge, which is essential for effective long-context processing (Wang
<table>
  <tr>
    <th>Lang.</th>
    <th>QA<sub>token</sub></th>
    <th>#QA</th>
    <th>Q<sub>token</sub></th>
    <th>A<sub>token</sub></th>
  </tr>
  <tr>
    <td>EN</td>
    <td>2.239</td>
    <td>17.23</td>
    <td>17.62</td>
    <td>110.19</td>
  </tr>
  <tr>
    <td>ZH</td>
    <td>2.852</td>
    <td>16.10</td>
    <td>36.44</td>
    <td>148.95</td>
  </tr>
  <tr>
    <td>JA</td>
    <td>2.934</td>
    <td>10.84</td>
    <td>34.55</td>
    <td>178.99</td>
  </tr>
</table>

Table 4: Statistics for both QA Pair Extraction and Question Answering Tasks. \( QA_{token} \) is the average token length of all QA pairs per meeting, #QA is the average number of QA pairs per meeting, and \( Q_{token} \) and \( A_{token} \) denote the average token lengths of a question and answer, respectively.

et al., 2024). For simplicity, we use the QA pairs described above for this task. As mentioned, the transcribed speech text can be divided into multiple sections, the QA task tests the LLMs’ capability to find evidence within that designated section, while other sections with similar but unrelated content act as noise. This setup ensures a focused assessment of the models’ information retrieval skills.

3.3 Benchmark Construction

3.3.1 Data Collection
We have established four criteria for the manual collection of audio files from financial meetings, covering a range of events such as public roadshows, brokerage strategy meetings, industry exchanges, and earnings presentations: (1) Timeliness: Most meetings should be from recent years; (2) Length: Preference is given to longer audio files; (3) Categorizability: The audio files must align with categories defined in the GICS; (4) Authoritativeness: All audio files are sourced from our financial firm partners and are protected by our copyright.

All audio files are transcribed into text using ASR toolkit Whisper,\footnote{https://openai.com/index/whisper/} followed by a thorough manual correction process. Strict measures are used to ensure that no sensitive or personally identifiable information is included in the transcripts.

3.3.2 Annotation Process and Quality Control
The datasets for manual annotation are drawn from various projects that recruit experienced analysts fluent in different languages. These annotators follow the annotation guidelines (See Figure 10 in Appendix F), undergo comprehensive training, and have access to onboarding and guidance materials. Additionally, other analysts review the annotators’ work and provide ongoing feedback.

• Transcribed Speech Correction: Annotators correct ASR-generated transcriptions with original audio files available for reference during the correction process.

• Summarization: The annotators segment each document into sections based on distinct topics, ensuring that only sections with clear boundaries are selected for summarization. For each valid section, annotators are encouraged to use simple sentences in the summary. Prior to the annotation process, annotators undergo extensive training and discussions to achieve a high level of consensus. In the annotation process, the original audio files are available for reference.

• QA Pair Extraction: With corrected speech documents, annotators manually extract financially relevant questions from the text. For each identified question, they search for corresponding answers in the subsequent content. Only questions with valid answers are retained for further analysis. This meticulous process guarantees the quality and relevance of the extracted QA pairs, significantly enhancing the dataset’s value for financial insights.

Figure 4 in Appendix A shows a screenshot of an annotated example. For additional complete examples, please refer to the attached file.

4 Experimentation

4.1 Experimental Settings

Models. We evaluate seven advanced long-context LLMs with context windows ranging from 16K to 1000K, including two API-based LLMs: GPT-4o-2024-08-06-128K (OpenAI, 2023) and GPT-3.5.turbo-0125-16K, as well as five open-source LLMs: GLM4-9B-Chat-1000K (Zeng et al., 2022), Llama3.1-8b-Instruct-128K (Dubey et al., 2024), Qwen2-7B-chat-128K (Yang et al., 2024a), Qwen2-72B-Instruct-128K, and Qwen2.5-72B-Instruct-128K (Yang et al., 2024b). All models support the languages in M³FinMeeting.

Prompts. We evaluate LLMs in a zero-shot setting. For summarization, we prompt the LLMs to implicitly identify document sections and generate individual summaries, which are then combined into a final document summary. For QA pair extraction, we first prompt the LLMs to extract all questions, then provide answers for each sequentially. For question answering, instead of addressing one
question at a time, we combine related questions into a single prompt, allowing the LLM to produce a comprehensive response that includes all the answers. This better aligns with real-world tasks, like writing reviews or reports, and reduces API calls. Prompt examples are provided in Appendix C.

Metrics. For summarization, as shown in Figure 1, the final summary consists of multiple section summaries. We evaluate this using precision, recall, and F1 scores rather than traditional metrics like BLEU and ROUGE, as they better reflect how well the generated section summaries align with the reference (gold) summaries. Specifically, we align the automatic and gold section summaries using a cosine similarity score above 0.75, calculated with the OpenAI Embedding model4. Appendix B gives details of the three metrics. Meanwhile, recent studies (Wang et al., 2023; Zhang et al., 2023; Liu et al., 2024b) show that GPT-4 (OpenAI, 2023) aligns closely with human evaluations. Therefore, we use GPT-4 (gpt-4-turbo-2024-04-09) as the judge (GPT-4-Judge) to evaluate document-level summaries based on five criteria: Coverage, Redundancy, Readability, Accuracy, and Consistency, with scores ranging from 0 to 100. We report the average GPT-4-Judge score, based on the prompt in Appendix C.

For QA pair extraction, we assess the quality of generated questions using precision, recall, and F1 scores, comparing them to the reference questions, similar to the summarization evaluation. Additionally, GPT-4-Judge evaluates the generated QA pairs against the gold pairs using the same five criteria, and we report the average score.

For question answering, we group all questions together and prompt the LLMs to answer them sequentially, repeating each question before generating its corresponding answer. Therefore, we evaluate performance using the same metrics as in QA pair extraction, assessing both the quality of repeated questions and the overall performance of the generated QA pairs.

For the three tasks, Appendix D presents performance metrics using BLEU (Papineni et al., 2002) and ROUGE (Lin and Hovy, 2002). Appendix E includes detailed performance across languages, lengths, and GICS sectors. To address potential self-bias in LLM evaluations (e.g., GPT-4-Judge favoring GPT-4-generated answers), we follow Bai et al. (2024a) and use Qwen-plus5 as an alternative judge model. Results, shown in Section 4.3, indicate minimal bias, as the performance trend from GPT-4-Judge and Qwen-plus-Judge are very consistent. Moreover, we perform a human evaluation and calculate Fleiss’ Kappa (Scott, 1995) to measure agreement between GPT-4-Judge and human annotators, further supporting our conclusions. The results are presented in Section 4.4.

4.2 Results

Main Results. Table 5 shows the main results over all meetings.6 From it, we have the following observations:

• Overall performance: The seven LLMs can be divided into three groups. Group 1 consists of Qwen2.5-72B-Instruct, Qwen2-72B-Instruct, and GPT-4o, all achieving an overall GPT-4-Judge score near or above 70.0. Among these, Qwen2.5-72B-Instruct performs best, followed by GPT-4o and Qwen2-72B-Instruct, which deliver comparable results. Group 2 includes Qwen2-7B-Instruct and GLM4-9B-Chat, both scoring around 60.0. Group 3 consists of GPT-3.5-turbo and LLaMA3.1-8B-Instruct, with LLaMA3.1-8B-Instruct outperforming GPT-3.5-turbo.

• Summarization: The precision, recall, and F1 scores for section-level summaries are all below 30%, indicating poor alignment between generated and gold section-level summaries. These low scores suggest that LLMs struggle both with semantic accuracy and document segmentation.

• QA Pair Extraction: The low precision, recall, and F1 scores suggest poor alignment between generated and gold questions.

4https://platform.openai.com/docs/models/embeddings (text-embedding-3-small)

5https://help.aliyun.com/zh/model-studio/developer-reference/what-is-qwen-llm

6The total cost for OpenAI API calls was about $2,500, while experiments with other LLMs use eight NVIDIA A100/80G GPUs.
<table>
  <tr>
    <th rowspan="2">Model</th>
    <th colspan="4">Summarization</th>
    <th colspan="4">QA Pair Extraction</th>
    <th colspan="4">Question Answering</th>
    <th colspan="4">Overall</th>
  </tr>
  <tr>
    <th>P.</th><th>R.</th><th>F1</th><th>GPT-4</th>
    <th>P.</th><th>R.</th><th>F1</th><th>GPT-4</th>
    <th>P.</th><th>R.</th><th>F1</th><th>GPT-4</th>
    <th>P.</th><th>R.</th><th>F1</th><th>GPT-4</th>
  </tr>
  <tr>
    <td>GPT-4o</td><td>27.82</td><td>12.07</td><td>16.83</td><td>73.61</td>
    <td>23.33</td><td>41.98</td><td>29.99</td><td>66.85</td>
    <td>93.93</td><td>93.16</td><td>93.55</td><td>71.79</td>
    <td>70.66</td>
  </tr>
  <tr>
    <td>GPT-3.5-turbo</td><td>17.13</td><td>9.39</td><td>12.13</td><td>44.56</td>
    <td>9.66</td><td>25.07</td><td>13.95</td><td>31.13</td>
    <td>84.16</td><td>92.60</td><td>88.18</td><td>42.78</td>
    <td>39.55</td>
  </tr>
  <tr>
    <td>GLM4-9B-Chat</td><td>10.30</td><td>11.26</td><td>10.76</td><td>67.71</td>
    <td>15.08</td><td>5.22</td><td>7.76</td><td>46.06</td>
    <td>93.37</td><td>92.20</td><td>92.78</td><td>67.72</td>
    <td>60.76</td>
  </tr>
  <tr>
    <td>LLaMA3.1-8B-Instruct</td><td>6.24</td><td>8.34</td><td>7.14</td><td>52.01</td>
    <td>13.05</td><td>7.30</td><td>9.37</td><td>44.64</td>
    <td>57.21</td><td>39.97</td><td>47.06</td><td>40.01</td>
    <td>45.76</td>
  </tr>
  <tr>
    <td>Qwen2-7B-Instruct</td><td>28.67</td><td>19.39</td><td>23.14</td><td>73.59</td>
    <td>11.98</td><td>16.40</td><td>13.85</td><td>37.33</td>
    <td>89.83</td><td>93.01</td><td>91.40</td><td>69.99</td>
    <td>60.71</td>
  </tr>
  <tr>
    <td>Qwen2-72B-Instruct</td><td><b>29.59</b></td><td><b>20.18</b></td><td><b>23.99</b></td><td><b>74.17</b></td>
    <td>22.43</td><td>28.82</td><td>25.22</td><td>60.85</td>
    <td>93.27</td><td><b>93.58</b></td><td>93.42</td><td><b>73.50</b></td>
    <td><b>69.66</b></td>
  </tr>
  <tr>
    <td>Qwen2.5-72B-Instruct</td><td>28.98</td><td>15.56</td><td>20.25</td><td>74.51</td>
    <td><b>32.61</b></td><td><b>45.65</b></td><td><b>38.41</b></td><td><b>68.03</b></td>
    <td><b>93.75</b></td><td><b>93.59</b></td><td><b>93.66</b></td><td><b>74.81</b></td>
    <td><b>72.54</b></td>
  </tr>
</table>

Table 5: Performance of LLMs on three evaluation tasks. The overall GPT-4-Judge score is the micro-average of its scores across all three tasks. Scores in bold/underline denote the top/second-best performances.

![Bar charts showing GPT-4-Judge scores over different languages, GICS sectors, and input lengths](page_186_624_1217_482.png)

Figure 2: Performance based on GPT-4-Judge scores across languages (a), GICS sectors (b), and input lengths (c).

For example, even the best-performing LLM, Qwen2.5-72B-Instruct, achieves only 45.65% recall, missing more than half of the gold questions. This highlights significant room for improvement in extracting relevant QA pairs.

• Question Answering: The performance of all LLMs—measured by precision, recall, F1, and GPT-4-Judge scores—is significantly higher than for QA pair extraction.7 This discrepancy is not surprising, as in the question answering task, the questions are explicitly provided in the prompt. High F1 scores (over 90%) show that most LLMs can follow instructions well and properly repeat questions.

Effect over Different Languages. Figure 2 (a) shows the GPT-4-Judge scores across three languages. Most models perform best in Japanese, but there is no clear advantage in either Chinese or English. A closer look reveals that LLMs are more

7One exception is LLaMA3.1-8B-Instruct, which has difficulty following instructions and often fails to repeat the questions, resulting in lower GPT-4-Judge scores for the question answering task.
consistent in Japanese, likely because they adhere to the instructions more effectively in this language. The three Qwen models perform similarly in the Summarization task across all languages, followed by the Question Answering task. However, their performance varies most in the QA pair extraction task, where Qwen2.5-72B-Instruct obtains the highest scores, outperforming Qwen2-7B-Instruct with GPT-4-Judge scores of 72.76, 63.59, and 83.45 for English, Chinese, and Japanese, respectively.

Effect over Different Sectors. Figure 2 (b) compares model performance across sectors. Communication Services, Consumer Discretionary, and IT generally achieve higher GPT-4-Judge scores in summarization and question answering. However, the performance trend becomes more complex for the QA pair extraction task, showing increased variability across sectors. Overall, the performance gaps among sectors for GPT-4o, Qwen2-72B-Instruct, and Qwen2.5-72B-Instruct are much smaller compared to GPT-3.5-turbo and LLaMA3.1-8B-Instruct.8 This suggests that the former models are less affected by sector differences.

Effect over Different Input Lengths. Figure 2 (c) compares the performance across varying input lengths. A key observation is the sharp drop in GPT-3.5-turbo’s performance when the input exceeds 15K tokens, due to its 16K token context limit. In contrast, both Qwen2.5-72B-Instruct and GPT-4o demonstrate stable and competent performance across the three tasks, particularly excelling in handling longer contexts exceeding 15K tokens. On the other hand, Qwen2-72B-Instruct shows a declining trend in the QA pair extraction task as input length increases, indicating a reduced capability to maintain performance with longer inputs in this specific task. Future research could explore structured modeling, as outlined by (Zhu et al., 2019), to improve handling of long input contexts.

Effect of RAG-based Question Answering. Instead of prompting the LLMs to answer a list of questions in a single response, we also explore RAG-based question answering, where the LLM answers questions individually based on retrieved document chunks. Following Wang et al. (2024), we divide the document into 1,024-token chunks. For embedding, we utilize the OpenAI Embedding model. Figure 3 compares the performance of

![Line graph showing performance (in GPT-4-Judge score) across different input lengths before or after adding RAG module. Baseline 1 refers to prompting the LLM to answer a list of questions, while Baseline 2 refers to answering one question per response.](page_1012_120_388_282.png)

Figure 3: Performance (in GPT-4-Judge score) across different input lengths before or after adding RAG module. Baseline 1 refers to prompting the LLM to answer a list of questions, while Baseline 2 refers to answering one question per response.

Qwen2.5-72B-Instruct before and after the integration of the RAG module. This comparison is based on a random selection of 10 meetings from each length set, resulting in a total of 50 meetings. The results indicate that for documents exceeding 15K tokens, answering all questions in a single response (Baseline 1) outperforms all other variants that answer questions one at a time. Additionally, for variants that respond to one question at a time in documents longer than 10K tokens, we observe that a larger context leads to better performance, specifically: Baseline 2 > RAG (top 5) > RAG (top 3) > RAG (top 1). Notably, RAG (with top 5) only surpasses the non-RAG variants for documents shorter than 10K tokens.

4.3 Performance Evaluated by Qwen-plus-Judge

In addition to the performance assessed by the GPT-4-Judge, as presented in Table 5, Table 6 shows the performance evaluated by the Qwen-Plus-Judge. The prompt templates used for the Qwen-Plus-Judge are the same as those for GPT-4-Judge, as illustrated in Figure 8 and Figure 9. The performance trends in Table 6 align closely with those in Table 5, where Qwen2.5-72B-Instruct remains the top-performing model, followed by GPT-4o and Qwen2-72B-Instruct, which show similar performance. Next are Qwen2-7B-Instruct and GLM4-9B-Chat, both of which also exhibit comparable performance. This consistency further demonstrates that Qwen-Plus-Judge is a reliable alternative evaluator.

8GPT-3.5-turbo fails for the Utilities sector due to input length exceeding its 16K token limit.
<table>
  <tr>
    <th>Model</th>
    <th>Summarization</th>
    <th>QA Pair Extraction</th>
    <th>Question Answering</th>
    <th>Overall</th>
  </tr>
  <tr>
    <td>GPT-4o</td>
    <td>76.01</td>
    <td>67.02</td>
    <td>69.84</td>
    <td>71.12</td>
  </tr>
  <tr>
    <td>GPT-3.5-turbo</td>
    <td>45.46</td>
    <td>31.43</td>
    <td>37.88</td>
    <td>38.48</td>
  </tr>
  <tr>
    <td>GLM4-9B-Chat</td>
    <td>68.20</td>
    <td>47.06</td>
    <td>60.39</td>
    <td>58.86</td>
  </tr>
  <tr>
    <td>LLaMA3.1-8B-Instruct</td>
    <td>51.46</td>
    <td>43.90</td>
    <td>32.05</td>
    <td>42.75</td>
  </tr>
  <tr>
    <td>Qwen2-7B-Instruct</td>
    <td>75.39</td>
    <td>42.65</td>
    <td>67.87</td>
    <td>62.38</td>
  </tr>
  <tr>
    <td>Qwen2-72B-Instruct</td>
    <td>76.22</td>
    <td>65.58</td>
    <td>70.73</td>
    <td>71.01</td>
  </tr>
  <tr>
    <td>Qwen2.5-72B-Instruct</td>
    <td><b>76.99</b></td>
    <td><b>70.25</b></td>
    <td><b>74.01</b></td>
    <td><b>73.85</b></td>
  </tr>
</table>

Table 6: The results using Qwen-plus as the judge model.

<table>
  <tr>
    <th>Evaluation Method</th>
    <th>Summarization</th>
    <th>QA Pair Extraction</th>
    <th>Question Answering</th>
  </tr>
  <tr>
    <td>GPT-4-Judge</td>
    <td>72.83</td>
    <td>60.76</td>
    <td>66.44</td>
  </tr>
  <tr>
    <td>Human Annotators</td>
    <td>3.68</td>
    <td>3.13</td>
    <td>3.36</td>
  </tr>
</table>

Table 7: Comparison of average performance ratings between GPT-4-Judge and human annotators. Note that GPT-4-Judge uses a 1-100 scale, while human annotators use a 1-5 scale.

<table>
  <tr>
    <th>Agreement</th>
    <th>Kappa</th>
  </tr>
  <tr>
    <td>GPT-4-Judge & Human Annotators</td>
    <td>0.701</td>
  </tr>
  <tr>
    <td>Human Annotators</td>
    <td>0.650</td>
  </tr>
</table>

Table 8: Fleiss’ Kappa score between GPT-4-Judge and human annotators.

4.4 Human Evaluation and Fleiss’ Kappa Agreement Between GPT-4-Judge and Human Evaluators

We randomly select 100 meetings and recruit five expert human annotators to assess the overall quality of GPT-4o’s responses, including summarization, QA pair extraction, and question answering. Each response is rated on a scale from 1 to 5, based on the same five criteria used to prompt GPT-4-Judge. The final score for each response is the average rating across the five annotators. Table 7 compares the performance assessed by GPT-4-Judge and human annotators.

Moreover, to assess the agreement among the six evaluators (five human annotators and GPT-4-Judge), we compute Fleiss’ Kappa score, a metric for inter-annotator agreement. Specifically, we calculate two types of Kappa scores: 1) the agreement among all six evaluators, where GPT-4-Judge’s ratings (on a 1-100 scale) are converted to a 1-5 scale by dividing by 20; and 2) the agreement among the five human annotators. As shown in Table 8, the agreement among GPT-4-Judge and human annotators is still higher than that among humans.

4.5 Performance in BLEU and ROUGE

The analysis of model performances, based on BLEU and ROUGE metrics, highlights notable differences across various tasks. Specifically, Qwen2-72B-Instruct excels in the summarization task, consistently generating concise and coherent summaries that underscore its strength in synthesizing information. Meanwhile, Qwen2.5-72B-Instruct leads in both QA pair extraction and question answering, demonstrating its adeptness at understanding queries and providing precise responses. This comparison underscores the unique advantages and task-specific expertise of each model, offering a more comprehensive insight into their capabilities. For detailed data and specific scores, please refer to the full table located in the appendix D.

5 Conclusion

In this paper, we have introduced M^3FinMeeting, a novel multilingual, multi-sector, and multi-task benchmark specifically designed to evaluate financial meeting understanding in large language models (LLMs). By incorporating real-world dialogue from financial meetings, our dataset fills significant gaps in existing benchmarks, which often rely on static sources like news articles and earnings reports. Supporting English, Chinese, and Japanese, and encompassing 11 industry sectors defined by GICS, M^3FinMeeting enables a comprehensive evaluation of LLMs through tasks such as summarization, question-answer pair extraction, and question answering. Experimental results with seven representative LLMs, including GPT-4o and Qwen2.5-72B-Instruct, demonstrate notable performance challenges, highlighting significant room for improvement and establishing M^3FinMeeting as a valuable resource for advancing research in financial language processing.
Acknowledgments

The authors express their heartfelt gratitude to the anonymous reviewers for their invaluable feedback and insightful comments, which greatly enhanced the quality of this work. We also offer our sincere appreciation to the members of the Qwen DianJin Team for their exceptional contributions, dedication, and hard work, which were instrumental in the success of this project. This work was supported by the Alibaba Innovative Research Program.

Limitations

Our work has several limitations.

• High Annotation Costs and Challenges: Both summarization and question-answer pair extraction require annotators to summarize content and extract question-answer pairs from audio recordings lasting 1-2 hours and text exceeding 10K tokens. This process relies heavily on the annotators’ professional expertise. Summarization, in particular, demands a high level of skill from professional analysts and often involves open-ended responses. Consequently, the annotation process necessitates a significant investment of time and effort.

• Limited Coverage for Question Answering: In the question-answering task, we focus exclusively from the extracted QA pairs. This limits our ability to evaluate the LLMs’ capacity to search for evidence within the document beyond the provided questions. As a result, the dataset may not fully capture the models’ potential in more complex reasoning scenarios that require deeper comprehension of the content.

• Evaluation Issues: The performance of summary alignment in summarization and question alignment in both question-answer pair extraction and question answering relies on the embeddings used. Due to budget constraints, we have not conducted a more extensive evaluation, which may affect the robustness of our findings.

Ethics

Our dataset is sourced from publicly mandated disclosures, such as earnings calls and roadshows. While this information is publicly available, we anonymize it to respect corporate preferences for dissemination. This ensures both the academic utility and ethical integrity of our benchmark. Specifically, we remove company identifiers and sensitive details using GPT-4, followed by manual verification, to maintain privacy without compromising research value. All data annotators are part of the funded projects, ensuring consistent and responsible data handling. The dataset, excluding original audio files, will be available online.

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A Examples of M^3FinMeeting

For brevity, Figure 4 presents a screenshot of an annotated M^3FinMeeting example. Complete examples are available in the attached file.

B Details of Precision, Recall and F1 Metrics

For summarization, as shown in Figure 1, we align the generated and gold section summaries based on a cosine similarity score above 0.75. Let us assume that there are m section summaries in the generated summarization and n section summaries in the reference (gold) summarization. After aligning the section summaries between the two, let \( m_a \) represent the number of generated section summaries aligned, and \( n_a \) represent the number of gold section summarized aligned. Note that \( m_a \) and \( n_a \) do not need to be equal, as one section summary from one side can be aligned to multiple section summaries on the other side. We compute the Precision, Recall, and F1 as follows:

\[
\text{Precision} = \frac{m_a}{m}, \tag{1}
\]
\[
\text{Recall} = \frac{n_a}{n}, \tag{2}
\]
\[
\text{F1} = \frac{2 * \text{Precision} * \text{Recall}}{\text{Precision} + \text{Recall}}. \tag{3}
\]

The Precision, Recall, and F1 scores for QA pair extraction and question answering are computed similarly, with the generated and gold section summaries replaced by generated and gold questions.

C Prompt Examples for Tasks in M^3FinMeeting

We use the same prompt templates, written in English, for English, Japanese, and Chinese. In these templates, we clearly specify that the output language must align with the meeting content.

Figure 5 and Figure 6 show the prompt template for the summarization task and the QA pair extraction task, respectively. Figure 7 displays the prompt template for the question answering task. In this template, all questions from the document are listed, and the LLMs are instructed to answer them in a single response.

When evaluating with GPT-4-Judge, Figure 8 presents the prompt template for the summarization task. Meanwhile, Figure 9 displays the prompt template for both the QA pair extraction task and the question-answering task.

D Performance in BLEU and ROUGE

Table 9 shows the overall performance in BLEU and ROUGE. It shows that Qwen2-72B-Instruct achieves the best performance in summarization while Qwen2.5-72B-Instruct leads in both QA pair extraction and question answering.

E Detailed Performance Across Languages, Lengths, and GICS Sectors

Table 10, Table 11, Table 12 show the performance of LLMs with respect to the languages, the length sets, and the GICS sectors, respectively.

F Annotation Guidelines

Figure 10 shows the annotation guideline of M^3FinMeeting.
{
    "Meeting_ID": "M_EN_001",
    "GICS_Industry": "Industrials",
    "Language": "EN",
    "ASR_Text": {
        "content": "Honorable Investors, good evening. Thank you for attending the [Company1] 2022 Annual Results Conference. I am [Role1]."
    },
    "Structured_Summary": {
        "content": [
            {
                "text": "In 2022, [Company1] achieved revenue of 267.5 billion, with a 29% year-on-year growth, and a net profit of 6.17 bil"
            },
            {
                "text": "Consumer business growth was 17%, accounting for 52%, driven primarily by sectors like apparel, footwear, beauty, a"
            },
            {
                "text": "Local business significantly reduced losses, improving net profit by 8.2 percentage points. International business"
            }
        ]
    },
    "QA_List": {
        "content": [
            {
                "question": "I am [Role1] from [Company1], thankful for leaders' time and insights on the time-sensitive express business. I"
                "answer": "Respected questioner, thank you for your focus. As outlined earlier, from both personal and company perspectives,"
            },
            {
                "question": "I am [Role3] from [Company2], expressing gratitude to [Company1] executives [Role4], [Role5], [Role6], and [Rol"
                "answer": "For this inquiry, let's have [Role9] respond. Firstly, regarding the overarching industry landscape, mergers like"
            },
            {
                "question": "Dear [Company1] leaders, good evening. I'm [Role18] from [Company6]. I'd like to ask about [Company1] and [Comp"
                "answer": "At a strategic level, business core positioning aims not to align cycles to maintain group steadiness, as reflect"
            },
            {
                "question": "Could the company provide us with guidance over this year's performance as a reference? Specifically, during Q1"
                "answer": "Thank you for the inquiry. The question pertains to yearly and first-quarter directionality, addressed separately"
            }
        ]
    }
}

Figure 4: Example of annotated M^3FinMeeting.

<table>
  <tr>
    <th rowspan="2">Model</th>
    <th colspan="4">Summarization</th>
    <th colspan="4">QA Pair Extraction</th>
    <th colspan="4">Question Answering</th>
  </tr>
  <tr>
    <th>B-4</th>
    <th>R-1</th>
    <th>R-2</th>
    <th>R-L</th>
    <th>B-4</th>
    <th>R-1</th>
    <th>R-2</th>
    <th>R-L</th>
    <th>B-4</th>
    <th>R-1</th>
    <th>R-2</th>
    <th>R-L</th>
  </tr>
  <tr>
    <td>GPT-4o</td>
    <td>9.47</td>
    <td>49.32</td>
    <td>19.02</td>
    <td>29.23</td>
    <td>1.58</td>
    <td>8.40</td>
    <td>3.57</td>
    <td>5.94</td>
    <td>5.21</td>
    <td>27.53</td>
    <td>11.50</td>
    <td>19.65</td>
  </tr>
  <tr>
    <td>GPT-3.5-turbo</td>
    <td>6.03</td>
    <td>35.02</td>
    <td>12.87</td>
    <td>20.05</td>
    <td>0.45</td>
    <td>2.38</td>
    <td>1.02</td>
    <td>1.71</td>
    <td>2.84</td>
    <td>16.55</td>
    <td>6.27</td>
    <td>11.72</td>
  </tr>
  <tr>
    <td>GLM-9B-Chat</td>
    <td>9.27</td>
    <td>47.35</td>
    <td>18.89</td>
    <td>29.59</td>
    <td>1.78</td>
    <td>5.26</td>
    <td>2.59</td>
    <td>3.80</td>
    <td>5.80</td>
    <td>28.01</td>
    <td>11.66</td>
    <td>20.27</td>
  </tr>
  <tr>
    <td>LLaMA3.1-8B-Instruct</td>
    <td>4.77</td>
    <td>36.37</td>
    <td>11.82</td>
    <td>21.38</td>
    <td>1.16</td>
    <td>3.97</td>
    <td>1.93</td>
    <td>2.98</td>
    <td>2.76</td>
    <td>14.20</td>
    <td>5.98</td>
    <td>10.58</td>
  </tr>
  <tr>
    <td>Qwen2-7B-Instruct</td>
    <td>14.72</td>
    <td>55.63</td>
    <td>24.12</td>
    <td>35.98</td>
    <td>0.34</td>
    <td>2.00</td>
    <td>0.75</td>
    <td>1.36</td>
    <td>6.67</td>
    <td>29.78</td>
    <td>11.87</td>
    <td>20.04</td>
  </tr>
  <tr>
    <td>Qwen2-72B-Instruct</td>
    <td><b>14.89</b></td>
    <td><b>56.35</b></td>
    <td><b>24.39</b></td>
    <td><b>36.37</b></td>
    <td>1.65</td>
    <td>6.66</td>
    <td>3.04</td>
    <td>4.81</td>
    <td>6.90</td>
    <td>30.39</td>
    <td>12.22</td>
    <td>21.15</td>
  </tr>
  <tr>
    <td>Qwen2.5-72B-Instruct</td>
    <td>11.71</td>
    <td>51.92</td>
    <td>20.84</td>
    <td>32.76</td>
    <td><b>2.71</b></td>
    <td><b>11.32</b></td>
    <td><b>5.04</b></td>
    <td><b>7.91</b></td>
    <td><b>7.44</b></td>
    <td><b>32.35</b></td>
    <td><b>13.65</b></td>
    <td><b>22.43</b></td>
  </tr>
</table>

Table 9: Performance (in BLEU and ROUGE) of LLMs on three evaluation tasks. Here B-4 is for BLEU-4, R-1/2/L for ROUGE-1/2/L.
## Role:
You are a meeting content analyst responsible for summarizing the core information from meetings. Please analyze based on the following requirements.

## Skill:
- Content Focus: Concentrate on substantive discussions, decisions, and key data from the meeting, ignoring procedural content, opening introductions, and host speeches.
- Information Extraction: Extract important financial metrics, market dynamics, management strategies, and interactive content from the provided meeting text. Digital information should be quoted accurately from the original text without any unauthorized additions.
- Formatted Output: Present the summary results in JSON format, ensuring that each summary element is complete and informative.
- Level of Detail: Ensure that the summary content is rich, and consolidate similar themes into a single paragraph to cover as many key insights as possible.

## Constraints:
- Ignore procedural content, opening introductions, and host remarks that lack substantial significance, and focus on substantive discussions and decisions.
- The content should be as thorough as possible, merging similar themes for clarity.
- The output language should match the input language.

## Meeting Text:
{meeting_note}

## OutputFormat:
- The output should follow the specified JSON structure:
[
    {
        "text": "Extracted key summary paragraph"
    },
    ...
]

Figure 5: Prompt template used for the summarization task.
### Skills:
- Precise Information Extraction: Distinguishing and extracting each specific question and answer from extensive meeting records.
- Content Rewriting Ability: Transforming colloquial meeting dialogue into clear, written expression.
- Structured Data Output: Strictly organizing and outputting all Q&A pairs according to JSON format standards, satisfying detailed format requirements.

## Constraints:
- Maintain the integrity and accuracy of the information, omitting no questions or answers.
- Rewrite content to streamline questions while ensuring the richness of the answers.
- Must adhere to the specified JSON output format.
- Output language must be consistent with the input meeting notes.

## Workflow:
- First, thoroughly review the meeting content to identify and separate each independent question and response paragraph.
- Next, rewrite each paragraph to ensure questions are concise and answers are detailed and formal.
- Finally, consolidate the processed information into JSON format, forming an organized list of Q&A pairs.

## Output Format:
- The output should be a JSON array containing multiple Q&A objects, each with "question" and "answer" sections, for example:
```json
[
    {
        "question": "",
        "answer": ""
    },
    {
        # second Q&A pair
    }
]
```

## Meeting Notes
{meeting_notes}

Figure 6: Prompt template used for the QA pair extraction task.
## Role:
Meeting Content Analysis Expert: Possesses the ability to deeply understand lengthy meeting texts and can accurately extract information to answer a list of questions related to the meeting.

## Skill:
-- Efficient Text Summarization: Quickly distills key information and details from meeting texts.
-- Question Response Matching: Precisely identifies relevant answers in the meeting content for each question on the list.

## Constraints:
-- Input Limitation: Long meeting text and question list have been provided.
-- Output Requirement: Answers to each question in the list formatted in JSON.
-- Output Language: Output language must remain consistent with the language of the meeting text.

## Workflow:
-- First, conduct a comprehensive analysis of the meeting text to extract core points.
-- Then, meticulously compare the content of the meeting against each question in the question list to find corresponding answers.
-- Finally, organize the answers to ensure they are direct and accurate for each question.

## Meeting Text:
{meeting_note}

## Question List:
{question_list}

## Output Format:
-- The output language must be consistent with that of the meeting text.
-- The output should follow the JSON structure below:
[
    {
        "question": "First question",
        "answer": "Answer to the first question"
    },
    ...
]
]

Figure 7: Prompt template used for the question answering task. All questions from a document are listed, and the LLMs are prompted to answer them in a single response.
## Role:
Evaluation Expert: A professionally competent AI assistant evaluation specialist, proficient in accurately assessing the quality of AI assistant responses based on established criteria, ensuring the accuracy and objectivity of the evaluation.

## Gold Answer:
{gold_answer}

## Assistant’s Predicted Answer:
{predicted_answer}

## Skill:
- Precision Matching: Accurately identify semantic consistency, numerical and sequential accuracy, and the presence of hallucinations in the AI assistant's response based on a gold standard answer.
- Key Point Extraction: Quickly identify and verify whether the response includes all necessary key information to meet the completeness requirements of the question answered.
- Unbiased Evaluation: Provide comprehensive and impartial evaluation explanations, strictly following scoring criteria to deliver a fair score.

## Constraints:
- Evaluation Criteria: The assessment is limited to the following core aspects:
  1. Coverage: The summary should include key information from the source document, ensuring a comprehensive reflection of the original content.
  2. Redundancy: The summary should avoid unnecessary repetition or lengthy expressions, such as repeated sentences or overused noun phrases.
  3. Readability: The summary should remain fluent and understandable, with clear logic and well-organized information, avoiding ambiguities.
  4. Accuracy: The truthfulness and correctness of the information must be guaranteed, ensuring that numbers, facts, and descriptions are consistent with the source document.
  5. Consistency: The information in the summary should be logically consistent throughout, avoiding contradictions or conflicting information.

## Workflow:
- Analyze and compare the assistant’s response with the gold standard answer.
- Determine the relevant compliance level according to the established evaluation criteria.
- Provide the final score, adhering to the specified format for output.

## Output Format:
- Scores should be integers from 1 to 100.
- Strictly follow the specified JSON format without any additional explanations.
{
    "coverage": "",   // Coverage score
    "redundancy": "", // Redundancy score
    "readability": "", // Readability score
    "accuracy": "",   // Accuracy score
    "consistency": "", // Consistency score
    "overall": ""     // Overall score
}

Figure 8: Prompt template utilized for assessing summarization with GPT-4.
## Role:
- Evaluation Expert: A professionally competent AI assistant evaluation specialist, proficient in accurately assessing the quality of Q&A pairs extracted by the AI assistant from meeting texts based on established criteria, ensuring the accuracy and objectivity of the evaluation.

## Gold Answer:
{gold_answer}

## Assistant’s Predicted Answer:
{predicted_answer}

## Skill:
- Precision Matching: Accurately identify semantic consistency, numerical and sequential accuracy, and the presence of hallucinations in the Q&A pairs extracted by the AI assistant based on the Gold Answer.
- Key Point Extraction: Quickly identify and verify whether the Q&A pairs contain all necessary information and context to meet the completeness requirements of the question answered.
- Unbiased Evaluation: Provide comprehensive and impartial evaluation explanations, strictly following scoring criteria to deliver a fair score.

## Constraints:
- Evaluation Criteria: The assessment is limited to the following core aspects:
  1. Coverage: The Q&A pairs should include key information present in the Gold Answer, maintaining the same quantity and ensuring a comprehensive reflection of the original content.
  2. Redundancy: The Q&A pairs should avoid unnecessary repetition, lengthy expressions, or content exceeding that of the Gold Answer, remaining concise and clear.
  3. Readability: The Q&A pairs should be fluent and understandable, logically clear, with a well-structured information layout, facilitating comprehension.
  4. Accuracy: The truthfulness and correctness of the information must be guaranteed, ensuring that the facts and descriptions in the Q&A pairs are consistent with the Gold Answer.
  5. Consistency: The question and answer in the Q&A pairs should be logically consistent throughout, avoiding contradictions or conflicting information, and align in language with the Gold Answer.

## Workflow:
- Analyze and compare the Q&A pairs extracted by the assistant with the gold standard Q&A pairs.
- Determine the relevant compliance level according to the established evaluation criteria.
- Provide the final score, adhering to the specified format for output.

## OutputFormat:
- Scores should be integers from 1 to 100.
- Strictly follow the specified JSON format without any additional explanations.
{
    "coverage": "",   // Coverage score
    "redundancy": "",  // Redundancy score
    "readability": "",  // Readability score
    "accuracy": "",   // Accuracy score
    "consistency": "",  // Consistency score
    "overall": ""     // Overall score
}

Figure 9: Prompt template utilized for assessing both QA pair extraction and question answering with GPT-4-Judge.
<table>
  <tr>
    <th rowspan="2">Model</th>
    <th colspan="4">Summarization</th>
    <th colspan="4">QA Pair Extraction</th>
    <th colspan="4">Question Answering</th>
    <th rowspan="2">Overall</th>
  </tr>
  <tr>
    <th>P.</th><th>R.</th><th>FI</th><th>GPT-4</th>
    <th>P.</th><th>R.</th><th>FI</th><th>GPT-4</th>
    <th>P.</th><th>R.</th><th>FI</th><th>GPT-4</th>
    <th>GPT-4</th>
  </tr>
  <tr>
    <th colspan="15">English</th>
  </tr>
  <tr>
    <td>GPT-4o</td><td>5.58</td><td>3.62</td><td>4.39</td><td>72.83</td><td><b>40.12</b></td><td>19.21</td><td>25.98</td><td>68.37</td><td>98.25</td><td><b>98.25</b></td><td>98.25</td><td>67.02</td><td>69.41</td>
  </tr>
  <tr>
    <td>GPT-3.5-turbo</td><td>6.84</td><td>4.17</td><td>5.18</td><td>33.68</td><td>19.32</td><td>3.31</td><td>5.65</td><td>26.70</td><td><b>98.58</b></td><td>36.27</td><td>53.03</td><td>35.68</td><td>32.02</td>
  </tr>
  <tr>
    <td>GLM4-9B-Chat</td><td>0.63</td><td>3.29</td><td>1.06</td><td>60.45</td><td>2.54</td><td>7.31</td><td>3.77</td><td>39.69</td><td>98.31</td><td>97.62</td><td>97.96</td><td>69.76</td><td>56.63</td>
  </tr>
  <tr>
    <td>LLaMA3.1-8B-Instruct</td><td>1.37</td><td>1.65</td><td>1.49</td><td>45.98</td><td>5.78</td><td>9.05</td><td>7.06</td><td>45.53</td><td>87.09</td><td>85.02</td><td>86.04</td><td>52.75</td><td>48.04</td>
  </tr>
  <tr>
    <td>Qwen2-7B-Instruct</td><td>13.15</td><td>12.84</td><td>12.99</td><td>72.71</td><td>3.91</td><td>2.03</td><td>2.67</td><td>29.85</td><td>97.75</td><td>86.01</td><td>91.50</td><td>71.95</td><td>58.16</td>
  </tr>
  <tr>
    <td>Qwen2-72B-Instruct</td><td><b>15.43</b></td><td><b>16.02</b></td><td><b>15.72</b></td><td>71.92</td><td>15.26</td><td>10.38</td><td>12.36</td><td>54.85</td><td>98.31</td><td>94.89</td><td>96.57</td><td>75.03</td><td>67.26</td>
  </tr>
  <tr>
    <td>Qwen2.5-72B-Instruct</td><td>6.40</td><td>4.72</td><td>5.51</td><td><b>73.94</b></td><td>39.81</td><td><b>22.57</b></td><td><b>28.81</b></td><td><b>72.76</b></td><td>98.22</td><td>96.45</td><td>97.33</td><td><b>76.76</b></td><td><b>74.48</b></td>
  </tr>
  <tr>
    <th colspan="15">Chinese</th>
  </tr>
  <tr>
    <td>GPT-4o</td><td>31.49</td><td>11.55</td><td>16.90</td><td>72.03</td><td>40.76</td><td>23.22</td><td>29.58</td><td>63.43</td><td>91.49</td><td>91.86</td><td>91.68</td><td>69.33</td><td>68.36</td>
  </tr>
  <tr>
    <td>GPT-3.5-turbo</td><td>18.34</td><td>4.52</td><td>7.24</td><td>48.42</td><td>25.11</td><td>6.64</td><td>10.51</td><td>31.41</td><td>92.20</td><td>59.67</td><td>72.45</td><td>47.42</td><td>42.87</td>
  </tr>
  <tr>
    <td>GLM4-9B-Chat</td><td>21.24</td><td>10.49</td><td>14.31</td><td>67.90</td><td>5.31</td><td>15.18</td><td>7.86</td><td>43.45</td><td>90.32</td><td>91.61</td><td>90.96</td><td>64.14</td><td>58.81</td>
  </tr>
  <tr>
    <td>LLaMA3.1-8B-Instruct</td><td>10.03</td><td>8.15</td><td>8.23</td><td>68.61</td><td>4.29</td><td>12.80</td><td>8.06</td><td>30.20</td><td>90.93</td><td>91.93</td><td>92.34</td><td>67.07</td><td>59.38</td>
  </tr>
  <tr>
    <td>Qwen2-7B-Instruct</td><td>28.69</td><td>16.22</td><td>20.72</td><td>71.68</td><td>20.89</td><td>12.74</td><td>15.83</td><td>40.47</td><td>91.66</td><td>88.38</td><td>89.99</td><td>66.75</td><td>59.95</td>
  </tr>
  <tr>
    <td>Qwen2-72B-Instruct</td><td>30.21</td><td><b>16.99</b></td><td><b>21.75</b></td><td>72.48</td><td>32.20</td><td>25.97</td><td>28.75</td><td>58.85</td><td>92.02</td><td>91.83</td><td>91.92</td><td>70.57</td><td>67.43</td>
  </tr>
  <tr>
    <td>Qwen2.5-72B-Instruct</td><td><b>32.38</b></td><td>14.63</td><td>20.16</td><td><b>72.74</b></td><td><b>45.82</b></td><td><b>34.47</b></td><td><b>39.34</b></td><td><b>63.59</b></td><td><b>92.22</b></td><td><b>92.48</b></td><td><b>92.35</b></td><td><b>71.88</b></td><td><b>69.49</b></td>
  </tr>
  <tr>
    <th colspan="15">Japanese</th>
  </tr>
  <tr>
    <td>GPT-4o</td><td>37.03</td><td>25.03</td><td>29.87</td><td>80.06</td><td>61.04</td><td>35.51</td><td>44.89</td><td>81.50</td><td>90.95</td><td><b>90.86</b></td><td><b>90.91</b></td><td>85.88</td><td>82.62</td>
  </tr>
  <tr>
    <td>GPT-3.5-turbo</td><td>24.14</td><td>16.77</td><td>19.79</td><td>39.75</td><td>41.95</td><td>5.53</td><td>9.77</td><td>35.55</td><td><b>91.98</b></td><td>17.98</td><td>30.09</td><td>27.36</td><td>34.68</td>
  </tr>
  <tr>
    <td>GLM4-9B-Chat</td><td>33.65</td><td>22.15</td><td>26.71</td><td>74.32</td><td>19.85</td><td>29.33</td><td>23.67</td><td>66.88</td><td>90.56</td><td>90.31</td><td>90.43</td><td>82.43</td><td>74.52</td>
  </tr>
  <tr>
    <td>LLaMA3.1-8B-Instruct</td><td>3.12</td><td>6.51</td><td>4.22</td><td>44.90</td><td>39.34</td><td>26.56</td><td>31.71</td><td>66.66</td><td>91.07</td><td>90.40</td><td>90.74</td><td>52.30</td><td>53.79</td>
  </tr>
  <tr>
    <td>Qwen2-7B-Instruct</td><td><b>43.32</b></td><td><b>50.81</b></td><td><b>46.77</b></td><td>82.42</td><td>13.92</td><td>6.64</td><td>8.99</td><td>31.82</td><td>91.07</td><td>90.40</td><td>90.74</td><td>83.17</td><td>67.20</td>
  </tr>
  <tr>
    <td>Qwen2-72B-Instruct</td><td>41.41</td><td>48.93</td><td>44.86</td><td><b>83.49</b></td><td>36.87</td><td>25.00</td><td>29.79</td><td>78.46</td><td>90.89</td><td>90.22</td><td>90.55</td><td>85.78</td><td>82.65</td>
  </tr>
  <tr>
    <td>Qwen2.5-72B-Instruct</td><td>35.67</td><td>34.91</td><td>35.29</td><td>82.40</td><td><b>67.74</b></td><td><b>46.49</b></td><td><b>55.14</b></td><td><b>83.45</b></td><td>89.75</td><td>89.66</td><td>89.70</td><td><b>86.95</b></td><td><b>84.10</b></td>
  </tr>
</table>

Table 10: Performance of LLMs on three evaluation tasks with different languages.
<table>
  <tr>
    <th rowspan="2">Model</th>
    <th colspan="4">Summarization</th>
    <th colspan="4">QA Pair Extraction</th>
    <th colspan="4">Question Answering</th>
    <th rowspan="2">Overall</th>
  </tr>
  <tr>
    <th>P.</th><th>R.</th><th>FI</th><th>GPT-4</th>
    <th>P.</th><th>R.</th><th>FI</th><th>GPT-4</th>
    <th>P.</th><th>R.</th><th>FI</th><th>GPT-4</th>
    <th>GPT-4</th>
  </tr>
  <tr>
    <td colspan="15"><b>Set1 (0-5K)</b></td>
  </tr>
  <tr>
    <td>GPT-4o</td><td>25.00</td><td>28.52</td><td>26.64</td><td>69.75</td><td>36.90</td><td>24.29</td><td>29.30</td><td>69.55</td><td>93.70</td><td>93.70</td><td>93.70</td><td>64.33</td><td>65.25</td>
  </tr>
  <tr>
    <td>GPT3.5-turbo</td><td>18.25</td><td>28.52</td><td>22.26</td><td>58.76</td><td>27.06</td><td>15.29</td><td>19.54</td><td>44.38</td><td>93.70</td><td>93.70</td><td>93.70</td><td>62.67</td><td>55.89</td>
  </tr>
  <tr>
    <td>GLM4-9B-Chat</td><td>23.10</td><td>26.17</td><td>24.54</td><td>66.76</td><td>14.27</td><td>16.19</td><td>15.17</td><td>39.20</td><td>93.77</td><td>90.26</td><td>91.98</td><td>57.33</td><td>56.24</td>
  </tr>
  <tr>
    <td>LLaMA3.1-8B-Instruct</td><td>10.63</td><td>19.14</td><td>13.67</td><td>54.35</td><td>16.94</td><td>17.24</td><td>17.09</td><td>39.90</td><td>15.84</td><td>38.23</td><td>22.40</td><td>31.81</td><td>42.96</td>
  </tr>
  <tr>
    <td>Qwen2-7B-Instruct</td><td>25.25</td><td>39.84</td><td>30.91</td><td>67.18</td><td>26.33</td><td>17.84</td><td>21.27</td><td>50.95</td><td>93.54</td><td>93.40</td><td>93.47</td><td>65.62</td><td>61.93</td>
  </tr>
  <tr>
    <td>Qwen2-72B-Instruct</td><td>23.82</td><td>39.45</td><td>29.71</td><td>70.29</td><td>25.78</td><td>23.69</td><td>24.69</td><td>56.48</td><td>93.55</td><td>93.55</td><td>93.55</td><td>66.24</td><td>64.74</td>
  </tr>
  <tr>
    <td>Qwen2.5-72B-Instruct</td><td>28.02</td><td>37.11</td><td>31.93</td><td>70.40</td><td>41.60</td><td>33.43</td><td>37.07</td><td>60.93</td><td>93.70</td><td>93.70</td><td>93.70</td><td>67.07</td><td>66.74</td>
  </tr>
  <tr>
    <td colspan="15"><b>Set2 (5-10K)</b></td>
  </tr>
  <tr>
    <td>GPT-4o</td><td>26.01</td><td>17.01</td><td>20.57</td><td>73.51</td><td>44.78</td><td>26.76</td><td>33.50</td><td>65.37</td><td>91.97</td><td>92.31</td><td>92.14</td><td>69.10</td><td>69.42</td>
  </tr>
  <tr>
    <td>GPT3.5-turbo</td><td>16.22</td><td>11.67</td><td>13.57</td><td>62.07</td><td>27.47</td><td>9.10</td><td>13.68</td><td>45.45</td><td>91.98</td><td>79.26</td><td>85.15</td><td>64.69</td><td>57.71</td>
  </tr>
  <tr>
    <td>GLM4-9B-Chat</td><td>16.34</td><td>13.75</td><td>14.93</td><td>68.46</td><td>17.11</td><td>15.05</td><td>16.01</td><td>45.31</td><td>90.55</td><td>90.75</td><td>90.65</td><td>63.73</td><td>59.10</td>
  </tr>
  <tr>
    <td>LLaMA3.1-8B-Instruct</td><td>9.90</td><td>12.22</td><td>10.94</td><td>56.25</td><td>4.63</td><td>13.27</td><td>6.87</td><td>41.96</td><td>25.73</td><td>42.29</td><td>31.99</td><td>30.91</td><td>43.95</td>
  </tr>
  <tr>
    <td>Qwen2-7B-Instruct</td><td>25.59</td><td>26.94</td><td>26.25</td><td>71.97</td><td>21.98</td><td>14.79</td><td>17.68</td><td>43.90</td><td>91.90</td><td>88.48</td><td>90.16</td><td>66.61</td><td>61.47</td>
  </tr>
  <tr>
    <td>Qwen2-72B-Instruct</td><td>25.41</td><td>27.01</td><td>26.19</td><td>73.39</td><td>37.96</td><td>30.70</td><td>33.93</td><td>61.36</td><td>92.20</td><td>92.20</td><td>92.20</td><td>70.09</td><td>68.57</td>
  </tr>
  <tr>
    <td>Qwen2.5-72B-Instruct</td><td>27.60</td><td>22.29</td><td>24.66</td><td>72.87</td><td>48.25</td><td>35.38</td><td>40.82</td><td>65.91</td><td>92.27</td><td>92.27</td><td>92.27</td><td>71.93</td><td>70.59</td>
  </tr>
  <tr>
    <td colspan="15"><b>Set3 (10-15K)</b></td>
  </tr>
  <tr>
    <td>GPT-4o</td><td>29.80</td><td>11.30</td><td>16.39</td><td>73.54</td><td>43.45</td><td>24.04</td><td>30.96</td><td>67.56</td><td>91.90</td><td>93.48</td><td>92.68</td><td>72.54</td><td>70.49</td>
  </tr>
  <tr>
    <td>GPT3.5-turbo</td><td>17.76</td><td>6.59</td><td>9.61</td><td>61.43</td><td>22.18</td><td>6.31</td><td>9.83</td><td>38.86</td><td>93.90</td><td>69.30</td><td>77.03</td><td>56.94</td><td>53.22</td>
  </tr>
  <tr>
    <td>GLM4-9B-Chat</td><td>16.05</td><td>9.92</td><td>10.03</td><td>68.80</td><td>3.01</td><td>17.21</td><td>5.15</td><td>48.28</td><td>31.86</td><td>91.48</td><td>92.67</td><td>68.44</td><td>61.42</td>
  </tr>
  <tr>
    <td>LLaMA3.1-8B-Instruct</td><td>9.93</td><td>8.80</td><td>8.07</td><td>51.80</td><td>3.85</td><td>14.01</td><td>10.85</td><td>30.85</td><td>52.85</td><td>52.85</td><td>52.85</td><td>46.16</td><td>44.48</td>
  </tr>
  <tr>
    <td>Qwen2-7B-Instruct</td><td>24.48</td><td>17.65</td><td>17.79</td><td>74.21</td><td>14.55</td><td>10.11</td><td>11.93</td><td>36.04</td><td>92.26</td><td>88.86</td><td>90.53</td><td>70.89</td><td>60.06</td>
  </tr>
  <tr>
    <td>Qwen2-72B-Instruct</td><td>30.68</td><td>18.97</td><td>23.44</td><td>73.43</td><td>32.47</td><td>25.39</td><td>28.49</td><td>61.37</td><td>92.62</td><td>91.72</td><td>92.17</td><td>74.14</td><td>69.66</td>
  </tr>
  <tr>
    <td>Qwen2.5-72B-Instruct</td><td>29.57</td><td>13.97</td><td>18.98</td><td>74.41</td><td>47.71</td><td>35.19</td><td>40.50</td><td>67.44</td><td>92.30</td><td>91.83</td><td>92.06</td><td>75.89</td><td>72.69</td>
  </tr>
  <tr>
    <td colspan="15"><b>Set4 (15-20K)</b></td>
  </tr>
  <tr>
    <td>GPT-4o</td><td>23.84</td><td>8.96</td><td>13.02</td><td>74.62</td><td>42.40</td><td>23.14</td><td>29.94</td><td>71.32</td><td>94.76</td><td>94.96</td><td>94.86</td><td>75.26</td><td>74.54</td>
  </tr>
  <tr>
    <td>GPT3.5-turbo</td><td>15.31</td><td>0.79</td><td>1.51</td><td>19.00</td><td>31.25</td><td>0.79</td><td>1.54</td><td>14.36</td><td>100.00</td><td>0.68</td><td>1.36</td><td>11.16</td><td>6.19</td>
  </tr>
  <tr>
    <td>GLM4-9B-Chat</td><td>15.27</td><td>10.92</td><td>12.73</td><td>67.41</td><td>8.49</td><td>15.01</td><td>10.85</td><td>50.09</td><td>93.82</td><td>94.81</td><td>94.31</td><td>71.86</td><td>64.54</td>
  </tr>
  <tr>
    <td>LLaMA3.1-8B-Instruct</td><td>7.13</td><td>6.31</td><td>6.69</td><td>45.04</td><td>18.48</td><td>13.27</td><td>15.45</td><td>48.97</td><td>70.59</td><td>74.30</td><td>52.89</td><td>50.52</td><td>41.83</td>
  </tr>
  <tr>
    <td>Qwen2-7B-Instruct</td><td>32.26</td><td>18.60</td><td>23.00</td><td>70.08</td><td>11.14</td><td>4.77</td><td>6.08</td><td>37.41</td><td>94.41</td><td>87.72</td><td>90.94</td><td>72.81</td><td>61.83</td>
  </tr>
  <tr>
    <td>Qwen2-72B-Instruct</td><td>34.69</td><td>19.87</td><td>25.27</td><td>76.69</td><td>29.87</td><td>16.11</td><td>18.18</td><td>62.49</td><td>94.90</td><td>92.65</td><td>94.27</td><td>76.97</td><td>72.91</td>
  </tr>
  <tr>
    <td>Qwen2.5-72B-Instruct</td><td>27.47</td><td>12.98</td><td>17.63</td><td>76.67</td><td>48.23</td><td>33.58</td><td>39.59</td><td>73.37</td><td>94.97</td><td>92.02</td><td>94.99</td><td>76.65</td><td>76.73</td>
  </tr>
  <tr>
    <td colspan="15"><b>Set5 (>20K)</b></td>
  </tr>
  <tr>
    <td>GPT-4o</td><td>37.81</td><td>9.24</td><td>14.86</td><td>75.01</td><td>44.14</td><td>17.68</td><td>25.25</td><td>65.42</td><td>92.36</td><td>89.04</td><td>90.67</td><td>69.99</td><td>70.91</td>
  </tr>
  <tr>
    <td>GPT3.5-turbo</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td>
  </tr>
  <tr>
    <td>GLM4-9B-Chat</td><td>3.90</td><td>8.02</td><td>5.25</td><td>65.84</td><td>6.18</td><td>11.42</td><td>8.02</td><td>41.12</td><td>90.19</td><td>92.27</td><td>91.22</td><td>70.89</td><td>58.63</td>
  </tr>
  <tr>
    <td>LLaMA3.1-8B-Instruct</td><td>1.89</td><td>5.73</td><td>2.84</td><td>52.82</td><td>9.45</td><td>10.68</td><td>10.03</td><td>45.14</td><td>77.85</td><td>77.35</td><td>77.60</td><td>47.42</td><td>47.13</td>
  </tr>
  <tr>
    <td>Qwen2-7B-Instruct</td><td>34.06</td><td>11.99</td><td>17.74</td><td>75.52</td><td>10.93</td><td>2.49</td><td>4.05</td><td>31.36</td><td>91.87</td><td>83.24</td><td>87.34</td><td>71.71</td><td>57.21</td>
  </tr>
  <tr>
    <td>Qwen2-72B-Instruct</td><td>32.30</td><td>11.92</td><td>17.41</td><td>75.70</td><td>16.67</td><td>8.84</td><td>11.55</td><td>59.15</td><td>92.63</td><td>90.34</td><td>91.42</td><td>76.33</td><td>69.83</td>
  </tr>
  <tr>
    <td>Qwen2.5-72B-Instruct</td><td>34.93</td><td>11.15</td><td>16.91</td><td>76.79</td><td>46.97</td><td>25.69</td><td>33.21</td><td>68.59</td><td>93.09</td><td>93.09</td><td>93.09</td><td>78.75</td><td>73.28</td>
  </tr>
</table>

Table 11: Performance of LLMs on three evaluation tasks with different length sets.
<table>
  <tr>
    <th rowspan="2">Model</th>
    <th colspan="4">Summarization</th>
    <th colspan="4">QA Pair Extraction</th>
    <th colspan="4">Question Answering</th>
    <th rowspan="2">Overall</th>
  </tr>
  <tr>
    <th>P.</th><th>R.</th><th>F1</th><th>GPT-4</th>
    <th>P.</th><th>R.</th><th>F1</th><th>GPT-4</th>
    <th>P.</th><th>R.</th><th>F1</th><th>GPT-4</th>
    <th>GPT-4</th>
  </tr>
  <tr>
    <th colspan="15">Communication Services</th>
  </tr>
  <tr>
    <td>GPT-4o</td><td>23.72</td><td>11.02</td><td>15.05</td><td>73.78</td><td>37.23</td><td>24.70</td><td>29.70</td><td>68.71</td><td>94.02</td><td>96.23</td><td>95.11</td><td>71.35</td><td>71.41</td>
  </tr>
  <tr>
    <td>Qwen2-72B-Instruct</td><td>23.05</td><td><b>18.63</b></td><td><b>20.61</b></td><td>74.94</td><td>30.57</td><td>26.11</td><td>28.17</td><td>62.55</td><td><b>95.29</b></td><td><b>95.29</b></td><td><b>95.29</b></td><td>73.45</td><td>70.55</td>
  </tr>
  <tr>
    <td>Qwen2.5-72B-Instruct</td><td>24.13</td><td>14.69</td><td>18.27</td><td>75.25</td><td>40.46</td><td>28.47</td><td>33.42</td><td>70.06</td><td>95.29</td><td>95.29</td><td>95.29</td><td>76.65</td><td>73.10</td>
  </tr>
  <tr>
    <th colspan="15">Consumer Discretionary</th>
  </tr>
  <tr>
    <td>GPT-4o</td><td>23.82</td><td>10.85</td><td>14.91</td><td>72.24</td><td>41.86</td><td>22.16</td><td>28.98</td><td>64.63</td><td>93.04</td><td>94.49</td><td>93.76</td><td>69.22</td><td>68.74</td>
  </tr>
  <tr>
    <td>Qwen2-72B-Instruct</td><td><b>25.04</b></td><td><b>17.08</b></td><td><b>20.31</b></td><td>72.61</td><td>29.95</td><td>23.38</td><td>26.26</td><td>60.16</td><td><b>93.68</b></td><td><b>93.55</b></td><td><b>93.61</b></td><td>71.03</td><td>68.00</td>
  </tr>
  <tr>
    <td>Qwen2.5-72B-Instruct</td><td>22.06</td><td>12.34</td><td>15.83</td><td>72.62</td><td><b>44.60</b></td><td><b>30.92</b></td><td><b>36.52</b></td><td><b>67.19</b></td><td>93.17</td><td>93.83</td><td>93.51</td><td><b>72.84</b></td><td><b>73.10</b></td>
  </tr>
  <tr>
    <th colspan="15">Consumer Staples</th>
  </tr>
  <tr>
    <td>GPT-4o</td><td><b>27.43</b></td><td>10.90</td><td>15.60</td><td>71.77</td><td>41.91</td><td>18.51</td><td>25.68</td><td>65.98</td><td>91.27</td><td><b>92.38</b></td><td>91.82</td><td>70.58</td><td>69.47</td>
  </tr>
  <tr>
    <td>Qwen2-72B-Instruct</td><td>26.63</td><td><b>16.92</b></td><td><b>20.70</b></td><td><b>73.83</b></td><td>32.41</td><td>22.08</td><td>26.27</td><td>60.14</td><td>91.76</td><td>91.07</td><td>91.41</td><td>71.71</td><td>68.63</td>
  </tr>
  <tr>
    <td>Qwen2.5-72B-Instruct</td><td>24.34</td><td>13.05</td><td>17.23</td><td>73.75</td><td><b>44.79</b></td><td><b>29.13</b></td><td><b>35.30</b></td><td><b>64.36</b></td><td><b>92.20</b></td><td>92.29</td><td><b>92.24</b></td><td>73.00</td><td>70.41</td>
  </tr>
  <tr>
    <th colspan="15">Energy</th>
  </tr>
  <tr>
    <td>GPT-4o</td><td>48.60</td><td>29.09</td><td>36.40</td><td>80.13</td><td><b>55.80</b></td><td>38.54</td><td>45.59</td><td><b>74.14</b></td><td><b>95.40</b></td><td><b>95.03</b></td><td><b>95.21</b></td><td>79.14</td><td>78.12</td>
  </tr>
  <tr>
    <td>Qwen2-72B-Instruct</td><td>50.53</td><td><b>47.49</b></td><td><b>48.96</b></td><td>81.26</td><td>26.93</td><td>25.19</td><td>26.03</td><td>58.57</td><td>95.38</td><td>94.65</td><td>95.01</td><td>82.81</td><td>75.17</td>
  </tr>
  <tr>
    <td>Qwen2.5-72B-Instruct</td><td>53.81</td><td>40.13</td><td>45.97</td><td>81.59</td><td>52.88</td><td><b>45.41</b></td><td><b>48.87</b></td><td>73.90</td><td><b>95.40</b></td><td><b>95.03</b></td><td><b>95.21</b></td><td><b>85.10</b></td><td><b>80.30</b></td>
  </tr>
  <tr>
    <th colspan="15">Financials</th>
  </tr>
  <tr>
    <td>GPT-4o</td><td>24.13</td><td>8.68</td><td>12.77</td><td>73.81</td><td>41.34</td><td>29.62</td><td>34.52</td><td>68.50</td><td>95.49</td><td>95.49</td><td>95.49</td><td>68.59</td><td>70.37</td>
  </tr>
  <tr>
    <td>Qwen2-72B-Instruct</td><td><b>29.62</b></td><td><b>16.87</b></td><td><b>21.50</b></td><td>71.37</td><td>28.38</td><td>24.31</td><td>26.19</td><td>61.41</td><td>94.47</td><td>90.82</td><td>92.61</td><td>72.61</td><td>68.52</td>
  </tr>
  <tr>
    <td>Qwen2.5-72B-Instruct</td><td>26.92</td><td>12.15</td><td>16.75</td><td>75.02</td><td><b>46.91</b></td><td><b>36.71</b></td><td><b>41.19</b></td><td>68.43</td><td>95.41</td><td>90.49</td><td>92.89</td><td><b>72.70</b></td><td><b>72.11</b></td>
  </tr>
  <tr>
    <th colspan="15">Healthcare</th>
  </tr>
  <tr>
    <td>GPT-4o</td><td>24.00</td><td>9.25</td><td>13.36</td><td>73.28</td><td>41.51</td><td>25.72</td><td>31.76</td><td>67.57</td><td><b>93.52</b></td><td><b>93.52</b></td><td><b>93.52</b></td><td>74.07</td><td>71.63</td>
  </tr>
  <tr>
    <td>Qwen2-72B-Instruct</td><td>28.67</td><td><b>16.97</b></td><td><b>21.32</b></td><td><b>73.72</b></td><td>22.22</td><td>20.71</td><td>21.44</td><td>58.41</td><td>93.41</td><td>89.48</td><td>91.40</td><td>75.59</td><td>69.57</td>
  </tr>
  <tr>
    <td>Qwen2.5-72B-Instruct</td><td>29.07</td><td>12.76</td><td>17.73</td><td>73.37</td><td><b>44.52</b></td><td><b>37.54</b></td><td><b>40.73</b></td><td>68.43</td><td>93.37</td><td>93.52</td><td>93.45</td><td>76.43</td><td><b>72.74</b></td>
  </tr>
  <tr>
    <th colspan="15">Industrials</th>
  </tr>
  <tr>
    <td>GPT-4o</td><td>27.15</td><td>10.56</td><td>15.20</td><td>72.13</td><td>43.81</td><td>23.08</td><td>30.23</td><td>63.86</td><td>92.22</td><td>89.85</td><td>91.02</td><td>68.74</td><td>68.28</td>
  </tr>
  <tr>
    <td>Qwen2-72B-Instruct</td><td>28.93</td><td><b>18.15</b></td><td><b>22.30</b></td><td>73.33</td><td>31.95</td><td>23.60</td><td>27.15</td><td>60.25</td><td>92.52</td><td>92.37</td><td>92.44</td><td>71.75</td><td>68.30</td>
  </tr>
  <tr>
    <td>Qwen2.5-72B-Instruct</td><td>29.35</td><td>13.53</td><td>18.52</td><td>73.94</td><td><b>50.84</b></td><td><b>36.27</b></td><td><b>42.34</b></td><td><b>65.46</b></td><td><b>92.53</b></td><td><b>92.53</b></td><td><b>92.53</b></td><td><b>73.67</b></td><td><b>71.05</b></td>
  </tr>
  <tr>
    <th colspan="15">Information Technology (IT)</th>
  </tr>
  <tr>
    <td>GPT-4o</td><td>23.82</td><td>11.02</td><td>15.07</td><td>75.28</td><td>46.21</td><td>23.66</td><td>31.30</td><td>70.25</td><td>92.25</td><td>93.07</td><td>92.66</td><td>73.47</td><td>73.05</td>
  </tr>
  <tr>
    <td>Qwen2-72B-Instruct</td><td><b>28.78</b></td><td><b>21.13</b></td><td><b>24.37</b></td><td><b>75.58</b></td><td>29.32</td><td>20.02</td><td>23.79</td><td>62.55</td><td>92.59</td><td>91.72</td><td>92.15</td><td>75.30</td><td>71.25</td>
  </tr>
  <tr>
    <td>Qwen2.5-72B-Instruct</td><td>27.38</td><td>16.29</td><td>20.42</td><td>75.44</td><td><b>49.45</b></td><td><b>32.00</b></td><td><b>38.85</b></td><td><b>72.26</b></td><td><b>92.66</b></td><td>92.77</td><td><b>92.72</b></td><td><b>76.67</b></td><td><b>74.80</b></td>
  </tr>
  <tr>
    <th colspan="15">Materials</th>
  </tr>
  <tr>
    <td>GPT-4o</td><td>30.17</td><td>13.71</td><td>18.85</td><td>70.63</td><td>42.80</td><td>27.95</td><td>33.81</td><td>64.58</td><td>91.34</td><td>91.56</td><td>91.45</td><td>73.04</td><td>69.5</td>
  </tr>
  <tr>
    <td>Qwen2-72B-Instruct</td><td><b>31.63</b></td><td><b>25.00</b></td><td><b>27.92</b></td><td><b>71.47</b></td><td>38.03</td><td>29.87</td><td>33.46</td><td>61.42</td><td><b>91.58</b></td><td><b>91.80</b></td><td><b>91.69</b></td><td>75.19</td><td>69.51</td>
  </tr>
  <tr>
    <td>Qwen2.5-72B-Instruct</td><td>29.36</td><td>18.54</td><td>22.73</td><td>70.53</td><td><b>49.67</b></td><td><b>36.86</b></td><td><b>42.32</b></td><td><b>65.96</b></td><td>91.56</td><td>91.56</td><td>91.56</td><td><b>76.42</b></td><td><b>70.94</b></td>
  </tr>
  <tr>
    <th colspan="15">Real Estate</th>
  </tr>
  <tr>
    <td>GPT-4o</td><td><b>66.66</b></td><td>38.16</td><td>48.54</td><td>82.23</td><td>38.80</td><td>40</td><td>39.39</td><td>74.50</td><td>89.70</td><td>93.84</td><td>91.72</td><td>78.88</td><td>79.17</td>
  </tr>
  <tr>
    <td>Qwen2-72B-Instruct</td><td>48.00</td><td>36.64</td><td>41.55</td><td>82.85</td><td>16.92</td><td>16.92</td><td>16.92</td><td>66.50</td><td>93.84</td><td>93.84</td><td>93.84</td><td>82.75</td><td>77.35</td>
  </tr>
  <tr>
    <td>Qwen2.5-72B-Instruct</td><td>61.29</td><td><b>43.51</b></td><td><b>50.89</b></td><td><b>83.54</b></td><td><b>48.27</b></td><td><b>43.07</b></td><td><b>45.52</b></td><td><b>67.25</b></td><td>93.84</td><td>93.84</td><td>93.84</td><td>83.75</td><td>79.10</td>
  </tr>
  <tr>
    <th colspan="15">Utilities</th>
  </tr>
  <tr>
    <td>GPT-4o</td><td><b>57.77</b></td><td>43.33</td><td>49.52</td><td>79.25</td><td>39.28</td><td>22.44</td><td>28.57</td><td>63.33</td><td>83.67</td><td>83.67</td><td>83.67</td><td>82.00</td><td>76.42</td>
  </tr>
  <tr>
    <td>Qwen2-72B-Instruct</td><td>48.64</td><td><b>60.00</b></td><td><b>53.73</b></td><td><b>80.25</b></td><td>22.72</td><td>20.40</td><td>21.50</td><td>62.33</td><td>83.67</td><td>83.67</td><td>83.67</td><td>85.67</td><td>77.35</td>
  </tr>
  <tr>
    <td>Qwen2.5-72B-Instruct</td><td>53.70</td><td>48.33</td><td>50.87</td><td>79.50</td><td><b>65.95</b></td><td><b>63.26</b></td><td><b>64.58</b></td><td><b>69.33</b></td><td>83.67</td><td>83.67</td><td>83.67</td><td><b>86.33</b></td><td><b>78.78</b></td>
  </tr>
</table>

Table 12: Performance of LLMs on three evaluation tasks with different GICS sectors. To save space, we only report the performance of three LLMs.
Annotation Guidelines

1. Purpose
These guidelines are intended to provide financial analysts with a clear framework for consistently annotating audio transcripts from financial roadshow meetings, extracting comprehensive and professional structured summaries and Q&A information. All information should be saved in JSON format to ensure consistency and repeatability.

2. Annotation Process
2.1 Listen to the Audio
Complete Listening: Analysts should listen to the entire meeting audio to ensure a full understanding of the content.
Note-taking: While listening, jot down key themes and important details for use in subsequent annotation steps.

2.2 Load ASR Text
Obtain and Verify: Obtain the automatic speech recognition (ASR) text of the audio.
Text Proofreading: Compare the ASR text with the actual audio to identify and correct apparent errors.

2.3 Professional Structured Summary Annotation
Theme and Information Extraction
Independent Analysis: Two analysts independently extract important themes and relevant key information from the text.
Themes and Information: Ensure each theme contains its key information.
JSON Format Saving
Save Format: Format each theme and its key information into a JSON structure, with each JSON element representing a thematic paragraph.
Merging and Review
Senior Analyst Review: A senior analyst reviews and consolidates the two reports to ensure completeness and accuracy.

Annotation Steps
Create Theme Object: Create a JSON object for each theme, specifying the theme name.
Record Key Information: Save all crucial information entries in the "Key Information" array.

2.4 Q&A Extraction Annotation
Question and Answer Identification
Mark Interaction: Mark the Q&A interaction parts in the text, ensuring the questioner and answerer are identified (e.g., speaker and audience).
Summarization and Extraction
Refine Summaries: Extract and summarize each Q&A interaction from the meeting.
JSON Format Saving
Save Format: Save the Q&A pairs in JSON format, with each Q&A pair being an individual JSON element.
Merging and Review
Comprehensive Integration: Two analysts independently annotate and a senior analyst consolidates the final results.

Annotation Steps
Create Q&A Object: Create a JSON object for each Q&A pair, recording the questioner and answerer.
Record Content: Record detailed content in the "Question" and "Answer" fields, respectively.

3. Annotation Check
Review and Confirm: A senior analyst reviews the annotations to ensure consistency, accuracy, and completeness.
Communicative Adjustment: If inconsistencies arise, the senior analyst should discuss with annotators to finalize the standard version.

4. Notes
Maintain Neutrality: Stay objective during the annotation process, avoiding personal views.
Team Collaboration: Record and discuss difficult-to-judge information with the team promptly.
Data Confidentiality: Ensure compliance with confidentiality and data protection regulations for the meeting content.

Figure 10: Annotation guidelines of M^3FinMeeting.

Reviewer paper summaries: The paper presents a new benchmark for LLMs in the financial sector called M3FinMeeting. The authors argue that current benchmarks do not meet all the requirements for testing LLMs' understanding of financial meetings (e.g., spanning multiple sectors and languages for to test a broad understanding of financial recourses). Their new benchmark includes multilingual (English, Chinese, and Japanese) and multi-sector data (e.g., Energy, Healthcare, Industrials, IT) on financial meetings based on real transcripts of publicly available audio recordings. The benchmark tests LLMs on summarizing, question answering, and question-answer pair extraction tasks. The paper provides a baseline for this benchmark using several popular open- and closed-source LLMs (i.e., GPT-4o, LLaMA 3.1, Qwen 2). They use different metrics (e.g., precision, recall, f1, and LLM as judge) to determine the performance against gold labels provided by human annotators. They find that there is room for improvement, especially for summarization and QA pair extraction tasks, and that even some of the latest long context models can't solve these tasks with high accuracy.

This paper presents M3FinMeeting, a comprehensive benchmark specifically crafted to evaluate how well large language models understand financial meetings. Unlike most existing datasets that are built from earnings reports or news articles, this benchmark focuses on real-world meeting transcripts, long, complex, and rich in decision-making dialogue, collected from 11 GICS industry sectors and in three languages: English, Chinese, and Japanese. It supports three key tasks: summarization, QA pair extraction, and question answering, all annotated by financial professionals to ensure quality. The authors benchmark seven LLMs, including GPT-4 and multiple Qwen models, and find that even top-performing models struggle, particularly with question-answer pair extraction and document segmentation in summarization, highlighting the challenges of long-context, multilingual understanding. The paper uses both GPT-4-based evaluations and human agreement studies to validate the benchmark’s robustness.
Summary of strengths: 1. This paper finds a gap in the currently existing financial datasets. Most of them are only in a single language. This work creates a dataset that is multilingual, multi-sector, and multi-task. 

2. The dataset creation process is thoroughly planned, involving manual corrections of transcribed speech, domain-expert annotations, and quality checks. Ensuring that the final dataset accurately reflects real financial meeting dynamics. 

3. The paper evaluates seven state-of-the-art LLMs (open and closed-source models like Llama, Qwen, GPT-4o), providing a clear performance baseline. This baseline highlights the limitations of even advanced models like GPT-4o. 

4. The use of diverse metrics (Precision, Recall, F1, GPT-4-Judge scores) alongside human evaluations and Fleiss’ Kappa agreement analysis ensures reliable assessment of model performance.

- The paper is clearly written and well-organized
- It introduces a new benchmark dataset  
- The dataset is multilingual, covering English, Chinese, and Japanese
Summary of weaknesses: 1. The paper does not demonstrate that higher scores on M3FinMeeting lead to improved performance on other financial tasks such as risk assessment or stock-movement prediction. As a result, it remains unclear whether these benchmarks evaluate fundamentally important capabilities or if they merely test skills confined to meeting transcripts. 

2. While the benchmark supports multiple languages, the corpora for each language are drawn from different financial meetings rather than being parallel or translations of the same content. This complicates direct cross-lingual comparisons, as any observed differences in model performance could stem from variations in subject matter rather than purely linguistic factors. The comparison of the performance across different languages doesn't take this into account/treats them as if the samples from different languages should have the same difficulty. Maybe the authors could consider investigating topics/samples that overlap between the selected languages. 

3. Although the paper highlights M3FinMeeting’s multilingual, multi-sector, and multi-task design, it does not provide a systematic comparison against other “multi-task” or “multi-domain” financial datasets (e.g., FLUE, FinanceBench, and other benchmarks mentioned in the paper). As a result, it remains unclear how M3FinMeeting’s approach surpasses or complements existing resources in terms of coverage or depth.

1. The paper does not provide sufficient detail about the sources of its financial meeting transcripts. There is a lack of transparency regarding where the data comes from, how it was selected, and whether there are potential biases or limitations in coverage.  
2. It remains unclear what concretely distinguishes the “financial meetings” in this benchmark from related materials like earnings calls, which are also live financial dialogues; the authors should clarify why existing meeting-style datasets like ECTSum are considered insufficient.  
3. The evaluation focuses on a limited set of general-purpose LLMs, with few financial-domain-specific models or language-specialized models considered, which weakens the benchmark's relevance for assessing domain-adapted or regional LLMs.  
4. The benchmark only supports three tasks (summarization, QA pair extraction, and QA), which, while relevant, are not sufficient to capture the full range of downstream financial meeting applications such as risk flagging, decision tracking, or action item detection.  
5. While the paper claims that annotation was conducted by trained analysts, there is no quantitative assessment of annotation quality, such as inter-annotator agreement or error analysis, which raises questions about the reliability and consistency of the labeled data.
6. The paper does not cite or compare against several recent and relevant benchmarks and models in the financial NLP space, including FinBen [1], Dólares or Dollars? [2], and two Japanese-focused efforts on continual pretraining and instruction tuning for financial LLMs [3][4]. These works cover a broader range of tasks, languages, and evaluation settings, and their omission limits the paper’s positioning within the current multilingual and domain-specific financial NLP research landscape.

[1] Xie, Qianqian, et al. "Finben: A holistic financial benchmark for large language models." Advances in Neural Information Processing Systems 37 (2024): 95716-95743.
[2] Zhang, Xiao, et al. "Dólares or dollars? unraveling the bilingual prowess of financial llms between spanish and english." Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2024.
[3] Hirano, Masanori, and Kentaro Imajo. "Construction of domain-specified japanese large language model for finance through continual pre-training." 2024 16th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI). IEEE, 2024.
[4] Tanabe, Kota, et al. "Jafin: Japanese financial instruction dataset." 2024 IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr). IEEE, 2024.
Comments, suggestions and typos: 1. Text and content of Figure 2 are too small and almost not readable. 

2. Figure 3 would be useful to display the baselines more distinct compared to the actual results

Please see Weaknesses.
Overall assessment scores: 2.5

1.5
Metareview score: 2
Metareview: This paper introduces a multilingual, multi-sector, and multi-task benchmark focused on understanding real-world financial meetings. The dataset covers transcripts in English, Chinese, and Japanese, drawn from a variety of industry sectors per GICS, and supports three main tasks: summarization, QA pair extraction, and question answering. Seven large language models (LLMs) are tested—both open-source and commercial—to highlight current challenges in handling complex, long-form financial dialogues.

Reviewers generally recognize the novelty of assembling a realistic, multilingual benchmark for long financial meetings. Some praise the thorough data curation process and the potential practical relevance to real-world financial scenarios. However, others identify significant areas of concern that prevent acceptance in the current form, including incomplete comparisons to related financial benchmarks, insufficient data transparency, and limited empirical evidence regarding annotation quality.

The dataset has promise as a resource for evaluating LLMs in real-world financial dialogue scenarios, but it requires clearer evidence of its novelty over existing benchmarks, more transparent documentation of data sourcing and licensing, deeper comparisons to relevant work, and stronger methodological or empirical validation of annotations and tasks.
Summary of reasons to publish: This benchmark highlights a dimension of financial dialogue—long, authentic meeting transcripts—that is less explored by current benchmarks

If improved and clarified, this dataset could become a useful resource for testing and developing LLMs in real-world financial discussions spanning multiple languages and sectors