Meeting the challenge: A benchmark corpus for automated Urdu meeting summarization

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3ACXCZ277J" target="_blank" >RIV/00216208:11320/25:CXCZ277J - isvavai.cz</a>

Výsledek na webu

<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85190131770&doi=10.1016%2fj.ipm.2024.103734&partnerID=40&md5=41bc0ab2008a8a59c01dfba52690d63b" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85190131770&doi=10.1016%2fj.ipm.2024.103734&partnerID=40&md5=41bc0ab2008a8a59c01dfba52690d63b</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.ipm.2024.103734" target="_blank" >10.1016/j.ipm.2024.103734</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Meeting the challenge: A benchmark corpus for automated Urdu meeting summarization

Popis výsledku v původním jazyce

Meeting summarization has become crucial as the world is gradually shifting towards remote work. Nowadays, automation of meeting summary generation is really needed in order to minimize the time and effort. The surge in online meetings has made summarization an indispensable requirement, yet summarizing Urdu meetings poses a formidable challenge due to the scarcity of pertinent corpora. Abstractively summarizing Urdu meetings compounds this challenge. This research addresses these gaps by introducing the Center for Language Engineering (CLE) Meeting Corpus, a benchmark resource tailored for meeting summarization in administrative and technical domains where Urdu is the primary language. Comprising 240 recorded meetings, encompassing both scenario-based and natural discussions, the corpus spans approximately 7900 min (∼132 h) of meeting duration. Beyond corpus creation, the study delves into the performance analysis of various deep learning models in Urdu abstractive meeting summarization. Models, including ur_mT5-small, ur_mT5-base, ur_mBART-large, ur_RoBERTa-urduhack-small, and GPT-3.5 with prompting, undergo comprehensive evaluation using both automated metrics and manual assessments based on five specific criteria. This research not only addresses the immediate challenges of Urdu meeting summarization but also contributes to advancing the capabilities of meeting summarization systems in diverse organizational contexts where Urdu is the language of communication during meetings. © 2024 Elsevier Ltd

Název v anglickém jazyce

Meeting the challenge: A benchmark corpus for automated Urdu meeting summarization

Popis výsledku anglicky

Meeting summarization has become crucial as the world is gradually shifting towards remote work. Nowadays, automation of meeting summary generation is really needed in order to minimize the time and effort. The surge in online meetings has made summarization an indispensable requirement, yet summarizing Urdu meetings poses a formidable challenge due to the scarcity of pertinent corpora. Abstractively summarizing Urdu meetings compounds this challenge. This research addresses these gaps by introducing the Center for Language Engineering (CLE) Meeting Corpus, a benchmark resource tailored for meeting summarization in administrative and technical domains where Urdu is the primary language. Comprising 240 recorded meetings, encompassing both scenario-based and natural discussions, the corpus spans approximately 7900 min (∼132 h) of meeting duration. Beyond corpus creation, the study delves into the performance analysis of various deep learning models in Urdu abstractive meeting summarization. Models, including ur_mT5-small, ur_mT5-base, ur_mBART-large, ur_RoBERTa-urduhack-small, and GPT-3.5 with prompting, undergo comprehensive evaluation using both automated metrics and manual assessments based on five specific criteria. This research not only addresses the immediate challenges of Urdu meeting summarization but also contributes to advancing the capabilities of meeting summarization systems in diverse organizational contexts where Urdu is the language of communication during meetings. © 2024 Elsevier Ltd

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

—

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Information Processing and Management

ISSN

0306-4573

e-ISSN

—

Svazek periodika

61

Číslo periodika v rámci svazku

2024

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

21

Strana od-do

1-21

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-85190131770