Unity in Diversity: Learning Distributed Heterogeneous Sentence Representation for Extractive Summarization
Table 1: Most similar sentences (from 1000-sentence subsample) in the SICK test data according to the Tree-LSTM. Tree/M/S denote relatedness (with the sentence preceding each group) predicted by the Tree-LSTM/MaLSTM/Siam-CSTI.
['[BOLD] Ranking by Dependency Tree-LSTM', '[BOLD] Tree', '[BOLD] M', '[BOLD] S']
it is evident that Siam-CSTI performs better (T-test with p-value=0.02) than similar state-of-the-art architectures: TreeLSTM We also experimented with basic TF-IDF cosine similarity and empirically found the superior performance of Siam-CSTI. The network seems to exploit the sequential and recursive aspects of the sentences to learn a rich set of semantics that help in identifying similar sentences.