A Sliding-Window Approach to Automatic Creation of Meeting Minutes (2021)

Abstract: Meeting minutes record any subject matter discussed, decisions reached and actions taken at the meeting. The importance of automatic minuting cannot be overstated. In this paper, we present a sliding window approach to automatic generation of meeting minutes. It aims at addressing issues pertaining to the nature of spoken text, including the lengthy transcript and lack of document structure, which make it difficult to identify salient content to be included in meeting minutes. Our approach combines a sliding-window approach and a neural abstractive summarizer to navigate through the raw transcript to find salient content. The approach is evaluated on transcripts of natural meeting conversations, where we compare results obtained for human transcripts and two versions of automatic transcripts and discuss how and to what extent the summarizer succeeds at capturing salient content.

## introduction meetings are ubiquitous across organizations of all shapes and sizes, and it takes a tremendous effort to record any subject matters discussed, final decisions reached and actions taken at meetings. with the rise of remote workforce, virtual meetings are more important than ever. an increasing number of video conferencing providers including zoom, microsoft team, amazon chime and google meet allow meetings to be transcribed @xcite . however, without automatic minuting, consolidating notes and creating meeting minutes is still regarded as a tedious and time-consuming task for meeting participants. there is thus an urgent need to develop advanced techniques to better summarize and organize meeting content. meeting summarization has been attempted on a small scale before the era of deep learning. previous work includes efforts to extract utterances and keyphrases from meeting transcripts @xcite @xcite , detect meeting decisions @xcite , compress or merge utterances to generate abstracts @xcite @xcite and make use of acoustic-prosodic and speaker features @xcite @xcite for utterance extraction. the continued development of automatic transcription and its easy accessibility have sparked a renewed interest in meeting summarization @xcite @xcite @xcite , where neural representations are explored for this task. we believe the time is therefore ripe for a reconsideration of the approach to automatic minuting. it may be tempting to apply neural abstractive summarization to meetings given its remarkable recent success on summarization benchmarks, e.g., cnn/dm @xcite @xcite . however, the challenge lies not only in handling hallucinations that are seen in abstractive models @xcite @xcite but also the models' strong positional bias that occurs as a consequence of fine-tuning on news articles @xcite . neural summarizers also assume a maximum sequence length, e.g., @xcite use the first 800 tokens of the document as input. with an estimated speaking rate of 122 words per minute @xcite , it indicates that the summarizer may only process a relatively short transcript -about 5 minutes in duration. in this paper, we instead study an extractive meeting summarizer to identify salient utterances from the transcripts. it leverages a sliding window to navigate through a transcript of any length and a neural abstractive summarizer to find salient local content. in particular, we aim to address three key questions: there are two obstacles that should be overcome in order for bart to generate meeting summaries from transcripts. firstly, bart is trained on written text, rather than spoken text. the pretraining data contain 160g of news, books, stories, and web text. it remains unclear if the model can effectively 1 https://cloud.google.com/speech-to-text identify salient content on spoken text and, how it is to reduce lead bias that is not as frequent in spoken text as in news writing @xcite . secondly, a transcript can far exceed the maximum input length of the model, which is restricted by the gpu memory size. this is the case even for recent variants such as reformer @xcite and longformer @xcite . ## our approach a sliding-window approach to generating meeting minutes is appealing because it breaks lengthy transcripts into small and manageable local windows, allowing a set of "mini-summaries" to be produced from such windows which are then assembled into meeting-level summaries. there are two essential decisions to be made when using a sliding window. firstly, one must decide on the size of the local window. our window size is bounded by the maximum sequence length of bart as the utterances in a window are concatenated into a flat sequence that serves as input to it. we consider a number of window sizes with @xmath0, 256, 512, 1024} tokens. secondly, a transcript may be partitioned into nonoverlapping or partially overlapping windows. we set the stride size to be @xmath1, 256, 512, 1024} tokens to support both (@xmath2. when they are of equal size, a transcript is divided into a sequence of non-overlapping windows. in figure 1 , we enumerate all 10 combinations of window and stride sizes. for example, we experiment with four window sizes of 128, 256, 512 and 1,024 tokens using the same stride size of 128 tokens, shown in dark blue (left). a larger window gives additional context to bart for recognizing salient content. using a window of 1,024 and stride of 128 tokens allow each utterance of the transcript to be visited 8 times, whereas using a window of 512 tokens reduces that to 4 times. consolidation. bart abstracts generated from local windows cannot be simply concatenated to form meeting-level summaries as they contain redundancy. when local windows are partially overlapping, they can cause the same content to be included in different abstracts. instead, we identify supporting utterances of each abstract from the transcript. particularly, we compute the rouge-l scores between each utterance in the window and the abstract. if the utterance is longer than 5 tokens, achieves a recall score @xmath3.5 and precision score @xmath4.1, we call it a supporting utterance. 3 the same utterance can support multiple abstracts. we include an utterance into the meeting summary if it is designated as the supporting utterance for at lease one local abstract. it lends flexibility and improves ease of consolidation of local abstractive summaries produced by bart. ## conclusion we investigate the feasibility of a sliding-window approach to generating meeting minutes and obtain promising results on both human and automatic transcripts. the approach does not require annotated data and it has a great potential to be extended to meetings of various domains. our future work includes, in the near horizon, experimenting with a look-ahead mechanism to enable the summarizer to skip over insignificant transcript segments.