Real-Time Summarization of Twitter
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Abstract (arXiv)
In this paper, we describe our approaches to TREC Real-Time Summarization of Twitter. We focus on real time push notification scenario, which requires a system monitors the stream of sampled tweets and returns the tweets relevant and novel to given interest profiles. Dirichlet score with and with very little smoothing (baseline) are employed to classify whether a tweet is relevant to a given interest profile. Using metrics including Mean Average Precision (MAP, cumulative gain (CG) and discount cumulative gain (DCG), the experiment indicates that our approach has a good performance. It is also desired to remove the redundant tweets from the pushing queue. Due to the precision limit, we only describe the algorithm in this paper.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 16 of its 34 references that are in the map. First posted on arXiv 11 Jul 2024.
Builds on
16 of 34 references.
References · 16
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- 2018Solution for Large-Scale Hierarchical Object Detection Datasets with Incomplete Annotation and Data Imbalancecited by 7
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