Batch Clustering for Multilingual News Streaming
Title:
Batch Clustering for Multilingual News Streaming
Authors:
Mathis Linger, Mhamed Hajaiej
Abstract
Nowadays, digital news articles are widely available, published by various editors and often written in di↵erent languages. This large volume
of diverse and unorganized information makes human reading very difficult or almost impossible. This leads to a need for algorithms able
to arrange high amount of multilingual news into stories. To this purpose, we extend previous works on Topic Detection and Tracking, and
propose a new system inspired from newsLens. We process articles per
batch, looking for monolingual local topics which are then linked across
time and languages. Here, we introduce a novel ”replaying” strategy to
link monolingual local topics into stories. Besides, we propose new fine
tuned multilingual embedding using SBERT to create crosslingual stories. Our system gives monolingual state-of-the-art results on dataset
of Spanish and German news and crosslingual state-of-the-art results
on English, Spanish and German news.
Proceedings : http://ceur-ws.org/Vol-2593/