{'fraction_non_alphanumeric': 0.028037383177570093, 'fraction_numerical': 0.0, 'mean_word_length': 5.395522388059701, '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}}
State-of-the-Art and Challenges in Timeline Summarization

Timeline Summarization (TLS) creates an overview of long-running events via dated daily summaries for the most important dates and is essential to keep track of a flood of information on, for example, crises data. TLS differs from standard single and multi-document summarization in the importance of date selection, interdependencies between summaries of different dates, the lack of large-scale human training data and a very low compression rate, i.e very short summaries in comparison to the number of corpus documents. In this talk, I will discuss the impact these properties have on (i) optimization algorithms and objective functions for timeline summarization algorithms (ii) evaluation of timeline summarization outputs and (iii) the dependence of TLS on information retrieval components.