VCSUM: A Versatile Chinese Meeting Summarization Dataset
Year: 2023

Abstract:
Compared to news and chat summarization, the development of meeting summarization is hugely decelerated by the limited data. To this end, we introduce a versatile Chinese meeting summarization dataset, dubbed VCSUM, consisting of 239 real-life meetings, with a total duration of over 230 hours. We claim our dataset is versatile because we provide the annotations of topic segmentation, headlines, segmentation summaries, overall meeting summaries, and salient sentences for each meeting transcript. As such, the dataset can adapt to various summarization tasks or methods, including segmentation-based summarization, multi-granularity summarization and retrievalthen-generate summarization. Our analysis confirms the effectiveness and robustness of VCSUM. We also provide a set of benchmark models regarding different downstream summarization tasks on VCSUM to facilitate further research. The dataset and code will be released at https://github.com/ hahahawu/VCSum.

heading: 1 Introduction
text: Meeting summarization (Janin et al., 2003; McCowan et al., 2005; Zhong et al., 2021) is the task of distilling the meeting transcript into a concise and readable summary that contains the most salient parts of a meeting. The summary can help the participants or absentees to quickly grape the highlight points. Therefore, a set of models have been proposed to comprehensively and succinctly summarize the content of a meeting (Zhu et al., 2020; Feng et al., 2021; Zhong et al., 2022b).
Compared to standard text summarization (Nallapati et al., 2016; Narayan et al., 2018), meeting summarization is a much more challenging task because it has more informal and oral expressions, topic shifts, multiple participants and longer context. For this reason, existing datasets for meeting
†Equal contribution.
summarization, i.e., AMI (McCowan et al., 2005) and ICSI (Janin et al., 2003), can hardly be used to train a robust summarization model owing to 1) their small size – AMI and ICSI only contain 137 and 59 pairs of meeting transcripts and summaries, respectively; 2) the specific domain – AMI just focuses on the product design and development while ICSI concentrates on the academic discussions; 3) coarse-grained summaries – the summaries in these two datasets are directly written for the whole meeting that might involve various topics. Although some larger dialogue summarization datasets have been created, e.g., SAMSum (Gliwa et al., 2019), DialogSUM (Chen et al., 2021) and
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MediaSum (Zhu et al., 2021), their context and summaries are still much shorter than the meeting summarization data. On the other hand, some variants of the summarization task are recently studied in news summarization, such as segmentationbased summarization (Liu et al., 2022) aiming at jointly segmenting and summarizing the lengthy context, multi-granularity summarization (Zhong et al., 2022a) aiming at generating coarse-, middleand fined-grained summaries at the same time, and retrieval-then-generate summarization (Mao et al., 2022) aiming at generating the summaries on the extracted salient sentences. While these variant tasks and methods have demonstrated their capacities on improving the news summarization performance, there is a huge demand for a large-scale versatile meeting summarization dataset to adapt these variants into the field.
To this end, we collect a Versatile Chinese meeting Summarization dataset based on the real-life meeting recordings, called VCSUM. The dataset contains 239 meetings, with a total duration of over 230 hours, and each meeting transcript has over 14K tokens on average. To make the dataset versatile, we provide various annotations, including segmentation-based annotations, multi-granularity annotations and extractive annotations. Table 1 illustrates an example from our dataset. The meeting transcript is segmented into several sections according to the topics discussed. Then multigranularity summaries are provided for each section, i.e., a coarse-grained headline summary with 5-20 words and a find-grained segmentation summary with 100-150 words. An overall summary with 200-250 words for the whole meeting is also annotated to formulate the challenging summarization tasks, such as lengthy meeting transcript summarization. Furthermore, we instruct annotators to highlight salient sentences in the meeting whose content is further verified to be highly consistent with the summaries. As such, our dataset is also a good testbed for the extract-then-generate summarization. In the experiment part, we evaluate several benchmark models on segmentation-based summarization, multi-granularity summarization, extract-then-generate summarization and highlight sentence extraction. We also provide a conversation solution to the dataset.
We summarize our contributions as follows: (1) we are the first one to collect a large and highquality meeting summarization dataset from real-
life videos in the last around 20 years; (2) we propose the first versatile summarization dataset that contains the annotations of extractive highlight sentences, topic segmentation, and multi-granularity summaries; (3) we conduct extensive experiments on the proposed dataset, constructing the benchmark to facilitate further research.

heading: 2 Related Work

heading: 2.1 Dialogue Summarization
text: Dialogue summarization aims to extract or summarize the most important information in the dialogue. Dialogue can take many forms, including chit-chat, meetings, and emails (Li et al., 2017; Zhang et al., 2021; Chen et al., 2021). A bunch of chat summarization datasets (Gliwa et al., 2019; Zhu et al., 2021; Chen et al., 2021) have been created yet. For example, the most widely used chat summarization dataset, i.e., SAMSum (Gliwa et al., 2019), is collected by linguists writing messenger-like conversations. This high-quality dataset greatly facilitates this research direction. A set of follow-up algorithms are proposed to solve the task by enhancing the dialogue context modeling(Chen and Yang, 2020; Zhong et al., 2022b) or dialogue participant modeling (Narayan et al., 2021). However, due to the much higher cost of collecting and annotating meetings, existing meeting summarization datasets are really limited, only AMI (McCowan et al., 2005) and ICSI (Janin et al., 2003), which were constructed around 20 years ago. The small size and inferior annotation quality of these two datasets are scarce to support the training of a robust meeting summarization model, while meeting summarization suffers more challenges than chat summarization, e.g., longer context and more topic shifts. Although some data augmentation techniques(Zhu et al., 2020; Zhong et al., 2022b) have been attempted, insufficient meeting summarization data remains a great barrier to this field moving forward. In this work, we create a highquality and larger meeting summarization dataset to fill the gap of no new meeting summarization datasets proposed in the last 20 years.

heading: 2.2 Advanced Summarization Tasks
text: Thanks to the abundant data and simpler annotations, many variants of the standard summarization task have been attempted on news summarization, including segmentation-based summarization (Liu et al., 2022), multi-granularity summarization
(Zhong et al., 2022a). The task of segmentationbased summarization is proposed to address the problem of multiple topics discussed in the lengthy document. The multi-granularity summarization aims to provide summaries with different degrees of semantic coverage. Although these tasks could improve the summarization performance, they cannot be tested on meeting summarization due to a lack of data. In this work, we provide the meeting summarization data with segmentation-based and multi-granularity annotations and build the benchmarks of these tasks on meeting summarization. Besides, superior to SEGNEWS dataset (Liu et al., 2022), VCSUM focuses on real-life meetings and provides human-annotated summaries.

heading: 3 The VCSUM Corpus

heading: 3.1 Data Selection
text: We collect the roundtable meetings from some Chinese video-sharing websites. We first obtain 1,419 videos from the websites by searching the keyword “圆桌会议(roundtable)”. To select high-quality videos and alleviate potential ethical issues, we crowdsource the basic meeting information by asking the following questions: 1) are the audio and video of the meeting clear? 2) how many valid participants in the meeting? Participants are valid only when they have lots of expressions and clearly articulate their opinions. 3) is there any reference in the meeting? The reference can be the supporting materials of the meeting, such as slides, technical documentation, requirement document, reports, etc. 4) does the meeting involve any offensive or ethical content, including politics, religious issues, gender concerns, or violence?
Each meeting will be marked by two annotators, and the disagreements will be tackled by the third annotator. Then the candidate videos are se-
lected by the conditions of 1) having clear audio and video; 2) the number of valid participants ranging from 1 to 10; 3) no references; 4) not involving any offensive or ethical concerns; and 5) the meeting duration ranging from 10 minutes to 200 minutes. We finally obtained 541 valid videos. The creation year of these videos ranges from 2017 to 2022. Owing to the limited budget for the annotation, we further selected 239 meetings, covering as many different domains as possible, including technology, finance, daily life and so on. We provide the topic distribution in Figure 1.

heading: 3.2 Data Annotation
text: The annotation is conducted on Feishu Minutes 1, an integrated platform for video parsing and automatic speech recognition (ASR). We upload candidate videos to the platform and parse them into meeting transcripts. Then annotators are asked to read the transcripts and provide five kinds of annotations.
Highlight sentences. Annotators should highlight the salient and informative sentences in the original meeting transcripts. The marked sentences must be fluent and complete. To avoid excessive labeling, the highlighted sentences should not exceed 10% of the entire transcript.
Topic segmentation. As a meeting generally involves multiple topics, we instruct annotators to identify different topic segments by inserting a special token [EOS] at the end of each segment. Note that the topic segmentation is annotated on the utterance level.
Segmentation headline. After obtaining topic segments, annotators should provide a headline to identify the topic of each segmentation. The word count for each headline should fall within the range of 5-20 words.
Segmentation summary. Annotators should write a summary for each segment. Different from headlines, segmentation summaries focus more on details of the content. Each segmentation summary should contain 100-150 words.
Overall summary. In the end, annotators should provide an overall summary that covers the most salient and informative content of the meeting. The word count for each overall summary should range from 200 to 250 words.
1https://meetings.feishu.cn/minutes

heading: 3.3 Quality Control
text: To obtain high-quality data, we release a set of data samples to select the most suitable and professional annotators. Finally, we recruit eight annotators majoring in law, finance or Chinese culture from the top universities in China (4 females and 4 males). Before the formal annotation, all annotators were asked to study the annotation protocols and practice on the training samples for a period of time. The annotation process began after all annotators passed our examination. We also have two annotation inspectors from our research group to monitor the whole process. During the annotation process, each sample is annotated by an annotator and checked by another annotator and an inspector. The annotation would be accepted only if both two checkers approved it. After the annotation, all results are further validated by ourselves. If any errors are found in an annotation batch, the corresponding annotator and checkers would be instructed to self-check and re-annotate the batch until the result meets our requirements. Additionally, to alleviate the error propagation from ASR, we manually compared several ASR technologies and finally selected Feishu Minutes. Our analysis of 100 randomly sampled meeting segmentations reveals a word error rate of 8%, which is significantly lower than the error rate of 30% reported in the AMI dataset. To further
improve the data quality, we encourage annotators to revise any typos or grammatical errors they find during the annotation process.

heading: 3.4 The Characteristics of VCSUM
text: Table 2 illustrates the comparison between VCSUM and other news, dialogue or meeting summarization datasets. We see that VCSUM has much longer context and summaries compared to existing news and dialogue summarization datasets. The transcripts in VCSUM contain 72.8 turns and 5.7 speakers on average, suggesting that the dataset also has the characteristics of multi-turn and multiparty dialogues. While the traditional meeting datasets, like AMI and ICSI, only focus on a single domain, VCSUM involves multiple domains, ranging from daily life to academic discussions. Moreover, we also provide the extractive highlight sentences in VCSUM. Each meeting transcript averagely has 71.7 highlighted sentences, with each sentence containing 32.5 tokens. We calculate the ROUGE scores (Lin, 2004) between highlight sentences and the corresponding summary. The larger value of ROUGE-1 indicates that the writ-
ten summaries are semantically consistent with the highlight annotations, while the smaller ROUGE-2 scores suggest the abstractiveness of the summary.
Previous study (Kedzie et al., 2018) reveals a critical problem of summarization task, called positional bias, which is that in existing datasets (McCowan et al., 2005; Chen et al., 2021; Zhu et al., 2021), most important information is often shown at the beginning of the context. This problem might bias the model to focus on the early words rather than the entire context. To this end, we also study the positional bias on our dataset. We evenly partition the transcript into 100 bins and count the frequency of the non-stop summary words appearing in each bin. As shown in Figure 2, the meeting transcripts contain more summary words near the beginning and end while segmentation transcripts hold more summary words in the middle part. However, the summary words of VCSUM are smoothly distributed in the transcripts. This observation indicates that our dataset does not suffer the positional bias, thus being a more challenging dataset.

heading: 4 Task Overview
text: Based on the annotation of our dataset, we propose three main challenging tasks. Given a meeting transcript C = {U1, U2, ..., UN} consisting of N utterances, wherein each utterance Ui = {wi1, wi2, ..., wi|Ui|} is a sequence of words, we formulate the tasks as follows.
Highlight Sentence Extraction The task of highlight sentence extraction (HSE) aims to find a set of spans H = {(wij , wik)|0 < i ≤ N, 0 < j < k ≤ |Ui|} that contain the most important information of the meeting. All highlight sentences are not overlapped and not across different utterances.
Segmentation-based Multi-granularity Summarization This task is essentially comprised of two sub-tasks, i.e., topic segmentation and multigranularity summarization. Formally, we aim to generate three summaries at different granularities, i.e., a headline Yh, a segmentation summary Ys and a joint summary Yh:Ys, based on the transcript segments S, where S ∈ {(Uj , ..., Uk)|0 < j ≤ k ≤ N}. The segments are partitioned in the level of utterance, and there are no overlapping among different segments.
Abstractive Meeting Summarization The goal of this task is to generate the overall summary Ygold
covering the diverse topics in the meeting based on the transcript C.

heading: 5 Experiments
text: To jointly accommodate the segmentation-based summarization and overall meeting summarization, we divide the train/dev/test datasets that contain around 80%/10%/10% segmentation summaries as well as overall summaries. There are totally 193/25/21 overall summaries and 1076/135/136 segmentation summaries in train/dev/test sets.

heading: 5.1 Highlight Sentence Extraction
text: Experiment Setup We solve the task as a sequence labeling problem. Due to the lengthy context of meeting transcripts, we try to split the transcript into small chunks. Here we evaluate the BERT (Devlin et al., 2019) and Longformer (Beltagy et al., 2020) models with chunk sizes of 512, 1024 and 2048. We report the F1 score and gold ROUGE-1 recall score as our evaluation metrics, wherein the gold ROUGE recall score is calculated between the generated highlight sentences and the gold overall meeting summary. Find the implementation details in Appendix B.
Results Table 4 illustrates the results of highlight sentence extraction on the test set. We can see that both BERT and Longformer perform better with the shorter input length. However, a huge performance drop is observed when BERT works on the longer inputs while Longformer could perform stably. This finding is consistent with the characteristics of these two models.

heading: 5.2 Segmentation-based Multi-granularity Summarization
text: Experiment Setup This task is comprised of two sub-tasks, i.e., topic segmentation and multi-
granularity summarization, wherein the summarization method is applied on the segmented sections.
For the topic segmentation, we formulate the task as a sequence labeling problem. We solve it by BERTSUMEXT model Liu and Lapata (2019) and BART encoder model (Lewis et al., 2020). Specifically, we insert a special token [CLS] at the beginning of each utterance, which would be used to classify whether the utterance is the end of a segment. Then we use an interval segmentation indicator to distinguish different utterances. However, due to the context length exceeding 10K in most samples, we split the meeting transcript into chunks where each chunk contains 5/10/15 turns of utterance. In the stage of inference, we make the predictions on segmented chunks but calculate the scores on the entire meeting transcript. Following previous work (Zhong et al., 2022b), we use the standard metrics Pk (Beeferman et al., 1999) and WinDiff (Pevzner and Hearst, 2002) to evaluate the segmentation models.
For multi-granularity summarization, we evaluate two settings, i.e., with predicted segments and with gold segments. For the former setting, the model is trained to first segment the transcript and then generate summaries for the predicted segments. For the latter, we provide the gold segments and evaluate the capacities of generating multi-granularity summaries with correct segments. As the number of predicted segments is uncertain, we calculate the ROUGE-1/2/L scores for each meeting by joining all predicted segmentation summaries together with a special token [Y_SEP]. We employ two widely-used summarization models, i.e., BART and Pegasus (Zhang et al., 2020), as our backbone. We also report the results of RANDOM
and ORACLE baselines. RANDOM is to randomly select sentences from the context as the summary, while ORACLE is to select the sentences with the highest ROUGE-1 scores against the ground truth.
In the setting of with predicted segments, we adopt the best-performing topic segmentation model, i.e., BERTSUMEXT trained with 10-turn chunks, to segment the transcripts. Then the summaries are generated by BART and Pegasus models on the segmented sections. Furthermore, we also try to initialize the encoder part of the generative BART with the weights from BARTenc. In this way, we expect the generative model can be aware of the segmentation features. Note that we do not follow the previous work (Liu et al., 2022) which jointly optimizes the segmentation and generation tasks with the pre-trained language models because our transcripts and summaries are much longer than theirs and the model cannot work well with the such lengthy context.
Results Table 5 illustrates the evaluation results of topic segmentation. As we can see, both BERTSUMEXT and BART models are much better than the baselines, i.e., RANDOM and EVEN. RANDOM is the baseline that randomly selects the utterances as the boundary of a segment, while EVEN is to partition the whole transcript evenly. BERTSUMEXT outperforms the BARTenc across the board. This is reasonable since BERTSUMEXT captures more inter-segment features by the interval indicator and high-level interact layers. The results of these two models also show the same trend that segmenting the transcript into 10 turns is the best choice. This is consistent with the findings in Sankar et al. (2019) that around 8 turns of dialogue context are enough to capture the contextual features.
Table 6 shows the evaluation results on segmentation-based multi-granularity summarization. When given gold segments, Pegasus (l = 2048) achieves the best performance on most metrics. Both BART and Pegasus models outperform the extractive oracle methods across the board, suggesting that the written summaries are abstractive enough. For the summary generation at different granularities, we find that the headline generation is harder than the summary generation owing to its highly condensed information. Comparing the generation of segmentation summary and joint summary, slight improvements are spotted when prompting the segmentation summary generation with the headline.
When evaluating with the predicted segments, we find that comparable performance could be reached with a strong topic segmentation model. After conducting more detailed analyses regarding the results of segmentation and generation, we surprisingly find that the most errors of our segmentation model are within three utterances, e.g., the label is the 10th utterance but the prediction is 8th, while the generation model can easily tolerate such deviations. An exception is tying the weights of the segmentation model with the generation model, i.e., BARTenc + BARTed, which performs much worse than others. We think this is because the generation process is not largely dependent on the segmentation features.

heading: 5.3 Abstractive Meeting Summarization
text: Experiment Setup It is a kind of long text summarization task (Liu et al., 2020; Gidiotis and Tsoumakas, 2020), which encourages the model to
generate a concise summary (< 512 words) containing the main content of a long text (> 5,000 words). We evaluate the vanilla sequence-to-sequence models and retrieval-then-generate methods. Specifically, we fine-tune the BART and Pegasus models with the truncation length of 1024 and 2048 to generate the overall summary. For retrievalthen-generate methods, we first retrieve the joint summaries or highlight sentences of a meeting transcript, and then produce the overall summaries on the retrieval results. This method hugely decreases the input length to the summarization model. We use the best-performing highlight model and joint summary generation model to finish the retrieval process. We report the ROUGE-1/2/L F1 scores for performance comparison.
Results As shown in Table 7, the vanilla models perform weakly on meeting summarization owing to the lengthy input. As said in Section 3.4, the summary words are evenly distributed in the entire transcript. Therefore, directly truncating the input would cause information loss. The paradigm of retrieval-then-generate is a good solution to this problem since it retrieves the key information first and then generates the summary on the retrieval results. When the summarization is based on gold content, the joint summary is a better ground than the highlight information. This is reasonable since the joint summary contains more concentrated information with fewer words. However, when the summarization is based on the retrieval results, predicted highlights even achieve comparable summarization performance to the gold highlights. This is attributed to the effectiveness of our highlight ex-
traction model and the redundancy information of highlight sentences. The worse performance from the predicted joint summary is because the generation of joint summaries is essentially a challenging task, which might cause more error propagation.

heading: 6 Further Discussions

heading: 6.1 Conversational Solutions
text: DIALOGUELM (Zhong et al., 2022b) is a strong baseline for meeting summarization in English. Based on the fact that meeting is a kind of long conversation, DIALOGUELM adopts sparse architecture and dialogue-specific pre-training objectives, such as speaker masking, turn splitting, turn merging and turn permutation, to capture the conversational features. It finally demonstrates that the pre-trained dialogue model is also a good solution to the meeting summarization task. Inspired by this observation, we make a preliminary attempt at the conversational solution to VCSUM. Specifically, we pre-train an encoder-decoder-based dialogue model, dubbed CONVLM, using our in-house Chinese dialogue data. We provide details of CONVLM in Appendix C. We train the model with the
objectives of speaker identification and response generation. After the pre-training, we fine-tune the model on VCSUM. To simplify the comparison, we evaluate on the task of multi-granularity summarization with gold segments.
As shown in Table 9, CONVLM achieves comparable or better performance against the summarization models. Especially for the generation of segmentation and joint summary, CONVLM consistently outperforms the baseline models, suggesting that modeling conversational features could benefit the summarization task on VCSUM. This finding demonstrates the conversational characteristics of our dataset and sheds light on using dialoguespecific pre-trained language models to solve the tasks of VCSUM.

heading: 6.2 Error Analysis
text: To study the difficulties of VCSUM, we take a detailed analysis of the error cases. We find that around 50% of errors is information missing which is essentially caused by lengthy input truncation. For example, in the first row of Table 8, the segmentation summary is much semantically close to the ground truth with only one key point (the red words) missing. This phenomenon is more severe when generating the overall summaries owing to the long and informative context. There are also some errors from irrelevant information or redundant information, accounting for around 20%. These kinds of errors are mostly found in the retrieval-then-generate methods that retrieve
some insignificant content in the first stage, thus finally misleading the generation process, like the blue words in Table 8. The remaining 30% errors include factual errors and syntactic errors.

heading: 7 Conclusion and Future Work
text: In this work, we collect a large and high-quality Chinese meeting summarization dataset from reallife videos, namely VCSUM. The dataset is versatile to support the tasks of highlight sentence extraction, segmentation-based summarization, multigranularity summarization and meeting summarization. Depth analyses demonstrate the superiority of our dataset. We then provide a strong benchmark for different downstream tasks on VCSUM. For future work, we believe the development of an endto-end framework that could jointly solve all tasks of VCSUM is a promising direction. Furthermore, we will also consider to release the video and audio files of the annotated meetings to facilitate the research of multi-modality meeting summarization.

heading: Acknowledgement
text: We sincerely appreciate the valuable and constructive comments from the reviewers. This work was supported in part by the Hong Kong ITF grant PRP/079/22FX, the Technological Breakthrough Project of Science, Technology and Innovation Commission of Shenzhen Municipality under Grants JSGG20201102162000001, InnoHK initiative, the Government of the HKSAR, Laboratory for AI-Powered Financial Technologies.
Limitations
A potential limitation of this work is that we just try some straightforward methods on the summarization tasks, such as vanilla generative pre-trained language models or pipeline retrieval-then-generate methods. We do not try the end-to-end two-stage approaches in this work since we are more focused on the contributions of the dataset construction and building the benchmark. We leave the development of advanced models as future work.
Ethics Statement
The construction of dataset. All videos in our newly-introduced dataset are available on the Chinese video sharing websites and are public to the download. To avoid the potential ethical issues, we carefully checked all videos in multiple aspects,
as said in Section 3.1. We try to guarantee that all videos do not involve any offensive, genderbiased, political content and any other ethical issues. During the annotation, we instruct annotators to anonymize or remove the sensitive or private information.
We recruit eight annotators that passed our examinations from the crowdsourcing platform, and two quality inspectors from our research team. To fairly paid the annotations, we first take an in-house annotation to evaluate the speed and difficulty of the large-scale annotations. Finally, we pay each annotator $25-$30 per hour. Typically, it would take around 2 hours to annotate a one-hour meeting. So, the workers are compensated $50-$60 per sample.
Applications. We will release the code and dataset along with friendly instructions to support its correct use. However, we still need to emphasize that abstractive summarization is a kind of generation task, which is not as controllable as we think. It still would generate some novel or unexpected words occasionally. Therefore, further research on the summarization faithfulness is warmly needed.

heading: A Translations
text: A.1 Translated Examples. See Table 10.
A.2 Translated Case Study See Table 11.
B Implementation
We provide the details of implementations here. For all experiments, we take five runs with different random seeds and report the average score.
Highlight Sentence Extraction We solve the task at sentence-level. Specifically, we split the utterances into small sentences by comma. Then, we insert a special token [CLS] at the beginning
of each sentence to represent the sentence. The binary classification is conducted on the special tokens to decide whether a sentence should be highlighted or not. Owing to the unbalanced ratio between positive and negative samples, we only active 40% negative samples in the training stage. The batch size is set to 16 or 32, depending on the chunk size. The models are optimized by Adam (Kingma and Ba, 2015) optimizer with an initial learning rate of 5e-5. The pre-trained BERT (hfl/chinese-roberta-wwm-ext-large) and Longformer (IDEA-CCNL/ErlangshenLongformer-330M) models are loaded from Huggingface2. We train the models for 10 epochs and select the best model on the validation set to evaluate on the test set. All experiments are conducted on 8 NVIDIA A100 GPUs.
Segmentation-based Multi-granularity Summarization For topic segmentation, the BERTSUMEXT is based on the large BERT model while
2https://huggingface.co/
BARTenc is based on large BART. We truncate each utterance with maximum utterance length of 128 words. The batch size is set to 16 or 32, depending on the chunk turn size. The other settings are mostly same to highlight sentence extraction.
For multi-granularity summarization, we employ the BART (fnlp/bart-large-chinese) and Pegasus (IDEA-CCNL/Randeng-Pegasus-523MSummary-Chinese) models from Huggingface as our backbones. The input sequence is truncated to the maximum length of 1024 or 2048. To incorporate speaker information, we add a speaker indicator at the beginning of each utterance, e.g., [Speaker_1: utterance_1; ...;Speaker_n: utterance_n;]. The batch size is set to 64. All models are optimized by Adam, and the learning rate is initialized to 5e-5 and linearly updated. During the training process, the best model is selected on the validation loss. In the stage of inference, we generate the summary using beam search with beam size of 5 and length penalty of 1.0.
Abstractive Meeting Summarization For the vanilla generative models, we truncate the input sequence to the maximum length of 1024 or 2048. The batch size is set to 64 or 32, depending on the truncation length. For the retrieval-then-generate methods, all predictions are obtained from the corresponding best-performing models, i.e., BERTSUMEXT+Pegasus(l = 1024) and BERT trained with chunk size of 512. During the decoding, we generate the summary using beam search with beam size of 5 and length penalty of 1.2.

heading: C Details of CONVLM
text: CONVLM employs the architecture of large BART model. The training data is collected from Chinese social media, including Zhihu3, Weibo4 and Douban5. We totally crawl 10 billion pieces of dialogue data, and select 10 million pieces of highquality data to train CONVLM. The pre-training objectives are speaker identification and response generation, wherein speaker identification is to predict the masked speaker indicators and response generation is to generate the response based on the context. We truncate the input sequence into 512. The batch size is set to 128. The learning rate is set to 5e-5 and linear updated along with the training process. We train the CONVLM for 300K steps,
3https://www.zhihu.com/ 4https://www.weibo.com/ 5https://www.douban.com/
which takes around 50 hours on 16 NVIDIA A100 GPUs. We validate the model each 1K steps. We save the model with the best validation loss and the last 10 checkpoints.

heading: D Screenshot of Annotation Platform
text: See Figure 3.
ACL 2023 Responsible NLP Checklist

heading: A For every submission:

heading: 3 A1. Did you describe the limitations of your work?
text: See the Limitation part.

heading: 3 A2. Did you discuss any potential risks of your work?
text: See the Ethics Statement part.

heading: 3 A3. Do the abstract and introduction summarize the paper’s main claims?
text: See the abstract and Section 1.
7 A4. Have you used AI writing assistants when working on this paper? Left blank.
B 3 Did you use or create scientific artifacts? See Section 3.
B1. Did you cite the creators of artifacts you used? Not applicable. Left blank.
7 B2. Did you discuss the license or terms for use and / or distribution of any artifacts? Upon acceptance, we will release our dataset and provide the guidelines for its use and distribution.
7 B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? Upon acceptance, we will release our dataset and provide the guidelines for its use and distribution.

heading: 3 B4. Did you discuss the steps taken to check whether the data that was collected / used contains any
text: information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? See Section 3.

heading: 3 B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and
text: linguistic phenomena, demographic groups represented, etc.? See Section 3.
3 B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. See Section 3.
C 3 Did you run computational experiments? See Section 5 and Appendix B.

heading: 3 C1. Did you report the number of parameters in the models used, the total computational budget
text: (e.g., GPU hours), and computing infrastructure used? See Appendix B.
The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a question on AI writing assistance.

heading: 3 C2. Did you discuss the experimental setup, including hyperparameter search and best-found
text: hyperparameter values? See Appendix B.

heading: 3 C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary
text: statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? See Appendix B.

heading: 3 C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did
text: you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? See Section 5 and Appendix B.
D 3 Did you use human annotators (e.g., crowdworkers) or research with human participants? See Section 3 and Ethics Statement.

heading: 3 D1. Did you report the full text of instructions given to participants, including e.g., screenshots,
text: disclaimers of any risks to participants or annotators, etc.? See Section 3 and Appendix D.

heading: 3 D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students)
text: and paid participants, and discuss if such payment is adequate given the participants’ demographic (e.g., country of residence)? See Section 3 and Ethics Statement.

heading: 3 D3. Did you discuss whether and how consent was obtained from people whose data you’re using/curating? For example, if you collected data via crowdsourcing, did your instructions to
text: crowdworkers explain how the data would be used? See Section 3 and Ethics Statement.

heading: 3 D4. Was the data collection protocol approved (or determined exempt) by an ethics review board?
text: See Section 3.

heading: 3 D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data?
text: See Section 3 and Ethics Statement.

heading: Future Work
text: We leave the development of advanced models as future work