How Document Pre-processing affects Keyphrase Extraction Performance
Table 2: Maximum recall and average number of keyphrase candidates for each model.
['[BOLD] Model TF×IDF', '[BOLD] Lvl 1 80.2%', '[BOLD] Lvl 1 7\u2009837', '[BOLD] Lvl 2 78.2%', '[BOLD] Lvl 2 6\u2009958', '[BOLD] Lvl 3 67.8%', '[BOLD] Lvl 3 2\u2009270']
Each model uses a distinct keyphrase candidate selection method that provides a trade-off between the highest attainable recall and the size of set of candidates. Syntax-based selection heuristics, as used by TopicRank and WINGNUS, are better suited to prune candidates that are unlikely to be keyphrases. As for KP-miner, removing infrequent candidates may seem rather blunt, but it turns out to be a simple yet effective pruning method when dealing with long documents. For details on how candidate selection methods affect keyphrase extraction, please refer to [Wang:2014:PAU:2649459.2649475].