Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Generating (Factual?) Narrative Summaries of RCTs: Experiments with
  Neural Multi-Document Summarization   We consider the problem of automatically generating a narrative biomedical
evidence summary from multiple trial reports. We evaluate modern neural models
for abstractive summarization of relevant article abstracts from systematic
reviews previously conducted by members of the Cochrane collaboration, using
the authors conclusions section of the review abstract as our target. We enlist
medical professionals to evaluate generated summaries, and we find that modern
summarization systems yield consistently fluent and relevant synopses, but that
they are not always factual. We propose new approaches that capitalize on
domain-specific models to inform summarization, e.g., by explicitly demarcating
snippets of inputs that convey key findings, and emphasizing the reports of
large and high-quality trials. We find that these strategies modestly improve
the factual accuracy of generated summaries. Finally, we propose a new method
for automatically evaluating the factuality of generated narrative evidence
syntheses using models that infer the directionality of reported findings.

21
The categories of this paper are: adap-org
4
The categories of this paper are: cond-mat
9
The categories of this paper are: nlin
36
The categories of this paper are: cmp-lg
11
The categories of this paper are: hep-lat
27
The categories of this paper are: comp-gas
5
The categories of this paper are: gr-qc
10
The categories of this paper are: q-bio
23
The categories of this paper are: solv-int
18
The categories of this paper are: econ
31
The categories of this paper are: ao-sci
32
The categories of this paper are: atom-ph
29
The categories of this paper are: funct-an
7
The categories of this paper are: hep-th
30
The categories of this paper are: q-alg
2
The categories of this paper are: math