Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Unified Linear-Time Framework for Sentence-Level Discourse Parsing   We propose an efficient neural framework for sentence-level discourse
analysis in accordance with Rhetorical Structure Theory (RST). Our framework
comprises a discourse segmenter to identify the elementary discourse units
(EDU) in a text, and a discourse parser that constructs a discourse tree in a
top-down fashion. Both the segmenter and the parser are based on Pointer
Networks and operate in linear time. Our segmenter yields an $F_1$ score of
95.4, and our parser achieves an $F_1$ score of 81.7 on the aggregated labeled
(relation) metric, surpassing previous approaches by a good margin and
approaching human agreement on both tasks (98.3 and 83.0 $F_1$).

9
The categories of this paper are: nlin
12
The categories of this paper are: nucl-th
20
The categories of this paper are: acc-phys
16
The categories of this paper are: stat
17
The categories of this paper are: q-fin
21
The categories of this paper are: adap-org
5
The categories of this paper are: gr-qc
24
The categories of this paper are: dg-ga
31
The categories of this paper are: ao-sci
37
The categories of this paper are: supr-con
26
The categories of this paper are: bayes-an
11
The categories of this paper are: hep-lat
7
The categories of this paper are: hep-th
33
The categories of this paper are: plasm-ph
13
The categories of this paper are: quant-ph
34
The categories of this paper are: chem-ph