TY - GEN
T1 - Vertext
T2 - 3rd ACM Conference on Computer-Supported Cooperative Work and Social Computing, CSCW 2020
AU - Anjum, Omer
AU - Chan, Chak Ho
AU - Lawphongpanich, Tanitpong
AU - Liang, Yucheng
AU - Tang, Tianyi
AU - Zhang, Shuchen
AU - Hwu, Wen Mei
AU - Xiong, Jinjun
AU - Patel, Sanjay
N1 - Publisher Copyright:
© 2020 Owner/Author.
PY - 2020/10/17
Y1 - 2020/10/17
N2 - Online communication platforms like Slack and Microsoft teams have become increasingly crucial for a digitized workplace to improve business efficiency and growth. However, these chat platforms can overwhelm the users with unstructured long streams of back and forth discussions scattered in various places. Thus, discussions become challenging to follow, leading to an increased likelihood of missing valuable information. Moreover, with the unsatisfying keyword-based chat search, users spend a significant amount of time to read, digest, and recall information from the conversations at the cost of productivity. In this paper, we present Vertext, an end-to-end AI system that ingests user conversations and automatically extracts information such as announcements, task assignments, and conversation summary. Moreover, Vertext gives a unique search experience to the users by providing search results along with their context, with an improved performance enabled by semantic search. For the ease of user interaction, all the information is consolidated on a single dashboard provided by Vertext.
AB - Online communication platforms like Slack and Microsoft teams have become increasingly crucial for a digitized workplace to improve business efficiency and growth. However, these chat platforms can overwhelm the users with unstructured long streams of back and forth discussions scattered in various places. Thus, discussions become challenging to follow, leading to an increased likelihood of missing valuable information. Moreover, with the unsatisfying keyword-based chat search, users spend a significant amount of time to read, digest, and recall information from the conversations at the cost of productivity. In this paper, we present Vertext, an end-to-end AI system that ingests user conversations and automatically extracts information such as announcements, task assignments, and conversation summary. Moreover, Vertext gives a unique search experience to the users by providing search results along with their context, with an improved performance enabled by semantic search. For the ease of user interaction, all the information is consolidated on a single dashboard provided by Vertext.
KW - Artificial intelligence
KW - Collaborative chat platforms for business
KW - Conversation disentanglement
KW - Deep average network
KW - Dialog act classification
KW - Microsoft teams
KW - Natural language processing
KW - Search engine for chat
KW - Semantic search
KW - Slack
KW - Transformer
UR - http://www.scopus.com/inward/record.url?scp=85095113893&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85095113893&partnerID=8YFLogxK
U2 - 10.1145/3406865.3418570
DO - 10.1145/3406865.3418570
M3 - Conference contribution
AN - SCOPUS:85095113893
T3 - Proceedings of the ACM Conference on Computer Supported Cooperative Work, CSCW
SP - 1
EP - 6
BT - CSCW 2020 Companion - Conference Companion Publication of the 2020 Computer Supported Cooperative Work and Social Computing
PB - Association for Computing Machinery
Y2 - 17 October 2020 through 21 October 2020
ER -