Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: MiRANews: Dataset and Benchmarks for Multi-Resource-Assisted News
  Summarization   One of the most challenging aspects of current single-document news
summarization is that the summary often contains 'extrinsic hallucinations',
i.e., facts that are not present in the source document, which are often
derived via world knowledge. This causes summarization systems to act more like
open-ended language models tending to hallucinate facts that are erroneous. In
this paper, we mitigate this problem with the help of multiple supplementary
resource documents assisting the task. We present a new dataset MiRANews and
benchmark existing summarization models. In contrast to multi-document
summarization, which addresses multiple events from several source documents,
we still aim at generating a summary for a single document. We show via data
analysis that it's not only the models which are to blame: more than 27% of
facts mentioned in the gold summaries of MiRANews are better grounded on
assisting documents than in the main source articles. An error analysis of
generated summaries from pretrained models fine-tuned on MiRANews reveals that
this has an even bigger effects on models: assisted summarization reduces 55%
of hallucinations when compared to single-document summarization models trained
on the main article only. Our code and data are available at
https://github.com/XinnuoXu/MiRANews.

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The categories of this paper are: funct-an
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The categories of this paper are: patt-sol
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The categories of this paper are: bayes-an
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The categories of this paper are: q-alg
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The categories of this paper are: cmp-lg
8
The categories of this paper are: hep-ex
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The categories of this paper are: stat
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The categories of this paper are: hep-th
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The categories of this paper are: astro-ph
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The categories of this paper are: chem-ph
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The categories of this paper are: solv-int
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The categories of this paper are: alg-geom
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The categories of this paper are: adap-org
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The categories of this paper are: gr-qc
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The categories of this paper are: nlin
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The categories of this paper are: hep-ph