{'fraction_non_alphanumeric': 0.028290282902829027, 'fraction_numerical': 0.00984009840098401, 'mean_word_length': 4.814285714285714, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
at TREC 2017: Real-Time Summarization Track

This paper presents the participation of Shanghai Normal University to the TREC 2017 Real-Time Summarization (RTS) Track. We adopted three different composed methods by applying various factors, i.e., count, cosine and distance to measure relevance between a tweet and a given topic. By setting static relevance threshold for each run, we selected the most relevant but non-redundant tweets and then pushed them to user’s phone in Scenario A. For Scenario B, we used a similar but much simpler approach. The evaluation results showed that there was still a long way to go in practice. Nonetheless, some progress has been made. We submitted three runs for both scenarios. This paper demonstrates the implementation details and official evaluation results of our system.