Table 2, we observe that the SPLITEM-BEDDING method, which is the embodiment of Scheme 1 (given in Section 2.1) where σ(w i , l j ), the semantic similarity of word w i with the label l j , is computed using embedding, yields the highest precision across all the methods.
sPLITMULTIEMBEDDING, a variant of scheme 2 (given in section 2.2) where the embeddings of section header and body are independently computed, and a weighted combination of the embeddings is used to retrieve the representative words of the section to compare with the embeddings of the labels, delivers the highest recall and Fscore values, as well as, the highest overall accuracy.