MUSED: A multimedia multi-document dataset for topic segmentation

Abstract

Research on topic segmentation has recently focused on segmenting documents by taking advantage of documents covering the same topics. In order to properly evaluate such approaches, a dataset of related documents is needed. However, existing datasets are limited in the number of related documents per domain. In addition, most of the available datasets do not consider documents from different media sources (PowerPoints, videos, etc.), which pose specific challenges to segmentation. We fill this gap with the MUltimedia SEgmentation Dataset (MUSED), a collection of documents manually segmented, from different media sources, in seven different domains, with an average of twenty related documents per domain. In this paper, we describe the process of building MUSED. A multi-annotator study is carried out to determine if it is possible to observe agreement among human judges and characterize their disagreement patterns. In addition, we use MUSED to compare the state-of-the-art topic segmentation techniques, including the ones that take advantage of related documents. Moreover, we study the impact of having documents from different media sources in the dataset. To the best of our knowledge, MUSED is the first dataset that allows a straightforward evaluation of both single- and multiple-documents topic segmentation techniques, as well as to study how these behave in the presence of documents from different media sources. Results show that some techniques are, indeed, sensitive to different media sources, and also that current multi-document segmentation models do not outperform previous models, pointing to a research line that needs to be boosted.

GeoBERTSegmenter: Word Segmentation of Chinese Texts in the Geoscience Domain Using the Improved BERT Model

“…Among the current mainstream CWS methods, the supervised character‐based tagging approach has yielded good word segmentation results (Mota et al., 2018; Üstün et al., 2021; Wei et al., 2021). However, this method requires a large number of well‐labeled corpora, and the word separation effect is generally better when the training corpus is tested with the same domain corpus.…”