Exploring Sentence Vector Spaces through Automatic Summarization

Even so, Table I suggests that the performance of the greedy algorithm is not based on the accuracy of the corresponding objective function. In particular, consider the two other strategies which try to maximize the same objective function: Brute force, and Maximum Similarity (which simply selects Greedy or Brute Force based on which one creates a summary with a higher cosine similarity). Brute Force consistently and significantly creates summaries with higher cosine similarity to the document, outperforming the Greedy selector on its objective function. By construction, the Max Similarity algorithm outperforms in cosine similarity to an even greater degree. But both of these algorithms perform much worse than the Greedy algorithm. Deeper analysis into the decisions of the Greedy algorithm reveals some reasons for this discrepancy. It appears that the good performance of the Greedy algorithm results not from the associated objective function, but by the way in which it maximizes this objective function. In particular, the Greedy algorithm selects sentences with low cosine similarity scores in a vacuum, but which increase the cosine similarity of the overall sentence (Figure 1). To understand why this is true, we consider the stepby-step behavior of the Greedy algorithm.