@inproceedings{elneima-etal-2026-media,
title = "Media-to-Insights: A Multi-Agent {AI} System for Continuous Media Monitoring, Analysis, and Reporting",
author = "Elneima, Ashraf Hatim and
Yilmaz, Ozan and
Nasrallah, Hadi and
Hewavitharana, Sanjika and
Al-Badrashiny, Mohamed and
Sawaf, Hassan",
editor = "Durrett, Greg and
Jian, Ping",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 3: System Demonstrations)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-demo.44/",
pages = "445--452",
ISBN = "979-8-89176-392-0",
abstract = "Continuous monitoring of high-volume media streams requires systems that go beyond keyword alerts to deliver structured, actionable intelligence. We present a multi-agent media monitoring system that processes streaming articles through three stages: (1) a Matching Agent that uses a hybrid keyword-then-semantic matching approach, reducing agent invocations by 2̃0{\%} (2) a batched multi-agent feature extraction, reducing core feature-extraction calls from 7 to 2 per article - a 71{\%} reduction - with bounded quality tradeoffs; and (3) a Report Generation Agent that uses deterministic deduplication and density-based clustering. Four autonomous life-cycle agents manage the evolution of watchers."
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<abstract>Continuous monitoring of high-volume media streams requires systems that go beyond keyword alerts to deliver structured, actionable intelligence. We present a multi-agent media monitoring system that processes streaming articles through three stages: (1) a Matching Agent that uses a hybrid keyword-then-semantic matching approach, reducing agent invocations by 2̃0% (2) a batched multi-agent feature extraction, reducing core feature-extraction calls from 7 to 2 per article - a 71% reduction - with bounded quality tradeoffs; and (3) a Report Generation Agent that uses deterministic deduplication and density-based clustering. Four autonomous life-cycle agents manage the evolution of watchers.</abstract>
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%0 Conference Proceedings
%T Media-to-Insights: A Multi-Agent AI System for Continuous Media Monitoring, Analysis, and Reporting
%A Elneima, Ashraf Hatim
%A Yilmaz, Ozan
%A Nasrallah, Hadi
%A Hewavitharana, Sanjika
%A Al-Badrashiny, Mohamed
%A Sawaf, Hassan
%Y Durrett, Greg
%Y Jian, Ping
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-392-0
%F elneima-etal-2026-media
%X Continuous monitoring of high-volume media streams requires systems that go beyond keyword alerts to deliver structured, actionable intelligence. We present a multi-agent media monitoring system that processes streaming articles through three stages: (1) a Matching Agent that uses a hybrid keyword-then-semantic matching approach, reducing agent invocations by 2̃0% (2) a batched multi-agent feature extraction, reducing core feature-extraction calls from 7 to 2 per article - a 71% reduction - with bounded quality tradeoffs; and (3) a Report Generation Agent that uses deterministic deduplication and density-based clustering. Four autonomous life-cycle agents manage the evolution of watchers.
%U https://aclanthology.org/2026.acl-demo.44/
%P 445-452
Markdown (Informal)
[Media-to-Insights: A Multi-Agent AI System for Continuous Media Monitoring, Analysis, and Reporting](https://aclanthology.org/2026.acl-demo.44/) (Elneima et al., ACL 2026)
ACL
- Ashraf Hatim Elneima, Ozan Yilmaz, Hadi Nasrallah, Sanjika Hewavitharana, Mohamed Al-Badrashiny, and Hassan Sawaf. 2026. Media-to-Insights: A Multi-Agent AI System for Continuous Media Monitoring, Analysis, and Reporting. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations), pages 445–452, San Diego, California, United States. Association for Computational Linguistics.