Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Ranking sentences from product description & bullets for better search   Products in an ecommerce catalog contain information-rich fields like
description and bullets that can be useful to extract entities (attributes)
using NER based systems. However, these fields are often verbose and contain
lot of information that is not relevant from a search perspective. Treating
each sentence within these fields equally can lead to poor full text match and
introduce problems in extracting attributes to develop ontologies, semantic
search etc. To address this issue, we describe two methods based on extractive
summarization with reinforcement learning by leveraging information in product
titles and search click through logs to rank sentences from bullets,
description, etc. Finally, we compare the accuracy of these two models.

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The categories of this paper are: nucl-th
34
The categories of this paper are: chem-ph
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The categories of this paper are: hep-lat
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The categories of this paper are: q-bio
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The categories of this paper are: nlin
29
The categories of this paper are: funct-an
7
The categories of this paper are: hep-th
5
The categories of this paper are: gr-qc
36
The categories of this paper are: cmp-lg
30
The categories of this paper are: q-alg
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The categories of this paper are: hep-ex
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The categories of this paper are: eess
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The categories of this paper are: patt-sol
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The categories of this paper are: astro-ph
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The categories of this paper are: econ
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The categories of this paper are: dg-ga