Summary of Real-time Summarization Of Twitter, by Yixin Jin et al.
Real-Time Summarization of Twitter
by Yixin Jin, Meiqi Wang, Meng Li, Wenjing Zhou, Yi Shen, Hao Liu
First submitted to arxiv on: 11 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Our research proposes approaches for real-time summarization of Twitter based on TREC Real-Time Summarization challenges. We focus on a push notification scenario where our system monitors sampled tweets and returns novel and relevant tweets matching given interest profiles. To classify tweet relevance, we employ Dirichlet score with minimal smoothing (baseline). Evaluation metrics such as Mean Average Precision (MAP), cumulative gain (CG), and discount cumulative gain (DCG) demonstrate good performance of our approach. Additionally, we aim to remove redundant tweets from the pushing queue due to precision limitations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary We developed a system that can quickly summarize Twitter updates based on user interests. Our method looks at recent tweets and finds ones that are new and important for each person’s profile. We used special scoring methods to see which tweets match each person’s interest. Our approach performed well in testing, but we also had to make sure our system didn’t send too many repetitive messages. |
Keywords
» Artificial intelligence » Mean average precision » Precision » Summarization
