PART-1
The Training data is in the form or a list of dictionaries (jsonl format). Each element in
the list is a distinct article that has an associated summary. The format of each element
of the list is shown below:
{'url': 'http://fortune.com/2014/10/14/luxury-spending-bain/',
'archive':
'http://web.archive.org/web/20141015014008id_/http://fortune.com/2014/10/1
4/luxury-spending-bain/',
'title': 'U.S., with help from Chinese tourists, boosts global luxury
spending',
'date': '20141015014008',
'text': 'Despite a crackdown on graft in China, Russia’s conflict with
the West over Ukraine and Europe’s sick-man economy, global luxury
spending is still on track to rise 5% in 2014, according to a report
released on Tuesday by consulting firm Bain & Co.\n\nBain, whose
projections are closely watched by the luxury industry, also stuck to its
forecast that global luxury goods sales will rise between
4% and 6% a year between 2014 and 2017.\n\nSo with all the insanity going
on in the world right now, what is giving luxury a much needed
boost? The good ol’ U.S. of A., which has become a bigger destination for
rich Chinese travelers (now that getting a visa is easier), and
where millennials are developing a taste for luxury as they progress in
their careers and start to make more money, said Claudia D’Arpizio,
a Bain partner in Milan and leader of the firm’s Global Luxury Goods and
Fashion Practice.\n\n“The U.S. is becoming a more and more important
tourist destination for Chinese travelers,” D’Arpizio told Fortune.
Chinese nationals spend three times more on luxury abroad than they do at
home.\n\nIndeed, Nordstrom JWN , Neiman Marcus and Saks Fifth Avenue are
all opening new Manhattan stores in the coming years to tap luxury’s
ongoing explosion in the U.S., fueled by tourism.\n\nU.S. consumer
confidence hit 14 month highs in September, and high end shopping has been
on a tear this year. The stock market has fallen sharply in the last week,
casting a shadow on consumer spending, but the downturn would need
to last for a while to hit luxury, D’Arpizio said. “We see consumer
confidence that is the most important driver.”\n\nThe uptick in the U.S.
comes at an opportune time for global luxury companies. In July, French
luxury group LVMH reported disappointing first-half results for 2012
while rival Kering, whose brands include Gucci, spoke of an “unsettled
business environment.” Meanwhile, L’Oreal said the U.S. rebound would
help its second-half results.\n\nBain, which conducted the study with
Italian luxury industry group Altagamma, said the global luxury market
is on target to reach 223 billion € ($283 bilion) in 2014, which
represents a 5% bump this year, a slower rate than the 7% last
year.\n\nAnd
there undoubtedly clouds in the luxury sky: Chinese sales have been hurt
by a crackdown by the government against corruption and conspicuous
consumption. And Russian luxury sales are down hurt by a drop in tourism
to Eastern European countries.',
'summary': 'What China, Russian slowdown? Luxury sales keep face with
forecast, Bain says.',
'compression': 30.5625,
'coverage': 0.75,
'density': 1.0,
'compression_bin': 'medium',
'coverage_bin': 'low',
'density_bin': 'abstractive'}
For the purposes of this exercise, you can ignore all keys other than the title, text and summary.
The task is to build multiple models using any deep learning tools that can output summaries for
any text article (title and text).
You must evaluate the models and choose your "winning" model.
PART-2
The NCBI disease corpus is fully annotated at the mention and concept level to serve as
a research resource for the biomedical natural language processing community.
Corpus Characteristics
● 793 PubMed abstracts
● 6,892 disease mentions
● 790 unique disease concepts
○ Medical Subject Headings (MeSH®)
○ Online Mendelian Inheritance in Man (OMIM®)
● 91% of the mentions map to a single disease concept
● divided into training, developing, and testing sets.
Corpus Annotation
● Fourteen annotators
● Two annotators per document (randomly paired)
● Three annotation phases
● Checked for corpus-wide consistency of annotations
The ask is to build a model to predict various Named Entities within the unlabelled text.
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