团队缩放@ AutoMin 2021:自动记录的跨域预训练

Felix Schneider, Sebastian Stüker, V. Parthasarathy
{"title":"团队缩放@ AutoMin 2021:自动记录的跨域预训练","authors":"Felix Schneider, Sebastian Stüker, V. Parthasarathy","doi":"10.21437/automin.2021-11","DOIUrl":null,"url":null,"abstract":"This Paper describes Zoom’s submission to the First Shared Task on Automatic Minuting at Interspeech 2021. We participated in Task A: generating abstractive summaries of meetings. For this task, we use a transformer-based summarization model which is first trained on data from a similar domain and then finetuned for domain transfer. In this configuration, our model does not yet produce usable summaries. We theorize that in the choice of pretraining corpus, the target side is more important than the source.","PeriodicalId":186820,"journal":{"name":"First Shared Task on Automatic Minuting at Interspeech 2021","volume":"4 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Team Zoom @ AutoMin 2021: Cross-domain Pretraining for Automatic Minuting\",\"authors\":\"Felix Schneider, Sebastian Stüker, V. Parthasarathy\",\"doi\":\"10.21437/automin.2021-11\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This Paper describes Zoom’s submission to the First Shared Task on Automatic Minuting at Interspeech 2021. We participated in Task A: generating abstractive summaries of meetings. For this task, we use a transformer-based summarization model which is first trained on data from a similar domain and then finetuned for domain transfer. In this configuration, our model does not yet produce usable summaries. We theorize that in the choice of pretraining corpus, the target side is more important than the source.\",\"PeriodicalId\":186820,\"journal\":{\"name\":\"First Shared Task on Automatic Minuting at Interspeech 2021\",\"volume\":\"4 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-09-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"First Shared Task on Automatic Minuting at Interspeech 2021\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.21437/automin.2021-11\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"First Shared Task on Automatic Minuting at Interspeech 2021","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.21437/automin.2021-11","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

摘要

本文描述了Zoom在Interspeech 2021上提交的第一个自动记录共享任务。我们参与了任务A:生成会议的抽象摘要。对于这项任务,我们使用基于变压器的汇总模型,该模型首先对来自相似域的数据进行训练,然后对域转移进行微调。在这个配置中,我们的模型还不能产生可用的摘要。我们认为在预训练语料库的选择中,目标侧比源侧更重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Team Zoom @ AutoMin 2021: Cross-domain Pretraining for Automatic Minuting
This Paper describes Zoom’s submission to the First Shared Task on Automatic Minuting at Interspeech 2021. We participated in Task A: generating abstractive summaries of meetings. For this task, we use a transformer-based summarization model which is first trained on data from a similar domain and then finetuned for domain transfer. In this configuration, our model does not yet produce usable summaries. We theorize that in the choice of pretraining corpus, the target side is more important than the source.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信