The essential task of Topic Detection and Tracking (TDT) is to organize a
collection of news media into clusters of stories that pertain to the same
real-world event. To apply TDT models to practical applications such as search
engines and discovery tools, human guidance is needed to pin down the scope of
an "event" for the corpus of interest. In this work in progress, we explore a
human-in-the-loop method that helps users iteratively fine-tune TDT algorithms
so that both the algorithms and the users themselves better understand the
nature of the events. We generate a visual overview of the entire corpus,
allowing the user to select regions of interest from the overview, and then ask
a series of questions to affirm (or reject) that the selected documents belong
to the same event. The answers to these questions supplement the training data
for the event similarity model that underlies the system.

本文介绍了一种人机交互的 TDT 模型方法，通过生成新闻语料库的可视化概览，用户能够选择感兴趣的区域并回答一系列问题，从而更好地理解不同事件及其共性，从而优化算法

人机交互主题检测与跟踪的主题时间热度图

Topic-time Heatmaps for Human-in-the-loop Topic Detection and Tracking

Nowadays, digital news articles are widely available, published by various
editors and often written in different 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.

提出了一种基于 Topic Detection and Tracking 的新闻处理系统，采用 “replaying” 策略将单语局部主题链接成故事，同时使用 SBERT 进行交叉语言处理，获得了在多个语言数据集上的最新成果。