  Sequence to sequence (Seq2Seq) learning has recently been used for
abstractive and extractive summarization. In current study, Seq2Seq models have
been used for eBay product description summarization. We propose a novel
Document-Context based Seq2Seq models using RNNs for abstractive and extractive
summarizations. Intuitively, this is similar to humans reading the title,
abstract or any other contextual information before reading the document. This
gives humans a high-level idea of what the document is about. We use this idea
and propose that Seq2Seq models should be started with contextual information
at the first time-step of the input to obtain better summaries. In this manner,
the output summaries are more document centric, than being generic, overcoming
one of the major hurdles of using generative models. We generate
document-context from user-behavior and seller provided information. We train
and evaluate our models on human-extracted-golden-summaries. The
document-contextual Seq2Seq models outperform standard Seq2Seq models.
Moreover, generating human extracted summaries is prohibitively expensive to
scale, we therefore propose a semi-supervised technique for extracting
approximate summaries and using it for training Seq2Seq models at scale.
Semi-supervised models are evaluated against human extracted summaries and are
found to be of similar efficacy. We provide side by side comparison for
abstractive and extractive summarizers (contextual and non-contextual) on same
evaluation dataset. Overall, we provide methodologies to use and evaluate the
proposed techniques for large document summarization. Furthermore, we found
these techniques to be highly effective, which is not the case with existing
techniques.
