Text Segmentation based on Semantic Word Embeddings
Table 5: Results on the arXiv test set for the C99 method using word vectors (oC99), our CVS method, and CVS method with normalized word vectors (CVSn). The Pk and WD metrics are given for both the greedy (G) and refined splitting strategies (R), with respect to the reference segmentation in the test set. The refined strategy was allowed up to 20 iterations to converge. The refinement converged for all of the CVS runs, but failed to converge for some documents in the test set under the C99 method. Refinement improved performance in all cases, and our CVS methods improve significantly over the C99 method for this task.
['Alg', 'S', '[ITALIC] Pk', 'WD']
Since much of the language of the scientific articles is specialized, the word vectors used in this case were obtained from GloVe trained on a corpus of similarly preprocessed texts from 98,392 arXiv articles. (Since the elements are now words rather than sentences, the only issue involves whether or not those word vectors are normalized.) As mentioned, the dynamic programming approach is prohibitively expensive for this dataset.