Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Does Structure Matter? Leveraging Data-to-Text Generation for Answering
  Complex Information Needs   In this work, our aim is to provide a structured answer in natural language
to a complex information need. Particularly, we envision using generative
models from the perspective of data-to-text generation. We propose the use of a
content selection and planning pipeline which aims at structuring the answer by
generating intermediate plans. The experimental evaluation is performed using
the TREC Complex Answer Retrieval (CAR) dataset. We evaluate both the generated
answer and its corresponding structure and show the effectiveness of
planning-based models in comparison to a text-to-text model.

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The categories of this paper are: quant-ph
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The categories of this paper are: bayes-an
35
The categories of this paper are: mtrl-th
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The categories of this paper are: acc-phys
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The categories of this paper are: hep-lat
25
The categories of this paper are: patt-sol
9
The categories of this paper are: nlin
28
The categories of this paper are: alg-geom
3
The categories of this paper are: physics
34
The categories of this paper are: chem-ph
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The categories of this paper are: nucl-th
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The categories of this paper are: q-fin
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The categories of this paper are: chao-dyn
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The categories of this paper are: funct-an
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The categories of this paper are: econ
10
The categories of this paper are: q-bio