Drowning in Research Reading? AI Could Help
Drowning in Research Reading? AI
Could Help
Artificial intelligence that reads journal articles
and highlights key findings could help researchers stay on top of the latest
research. But the technology isn’t ready for prime time.
By Lindsay McKenzie May
14, 2019
ISTOCKPHOTO.COM/ARTISTEER
Summarizing
the findings of a complex and technical research paper into plain English is no
easy feat, but a recent development by scientists at the Massachusetts Institute of Technology could
change that.
Using
a form of artificial intelligence called a neural network, scientists at MIT
and the Qatar Computing Research Institute at Hamad Bin Khalifa University have
created technology that can read scientific papers and generate easy-to-read
summaries that are just one or two sentences long.
The
research, recently published in the journal Transactions of the Association for Computational
Linguistics, could potentially be used by journalists to
help communicate complex research to the public, though the authors say they
aren't going to be putting journalists out of a job any time soon. (Phew.)
The
technology could, however, be used in the near future to tackle a long-standing
problem for scientists -- how to keep up with the latest research.
“The
problem of making sense of the millions of scientific papers published every
year is fundamental to accelerating scientific progress,” said Niki Kittur,
professor at the Human-Computer Interaction Institute at Carnegie Mellon
University, who was not involved in the research.
“Not
only is it difficult for researchers to keep up with a single field; some of
the greatest breakthroughs have historically been made by finding connections
between fields,” said Kittur. “Research like this could help scientists sift
through individual papers and get a faster understanding of what research would
be relevant to them, which is an important first step.”
Kittur
warned, however, that researchers are still far from developing AI that can
“deeply understand a paper’s contributions, let alone synthesize across papers
to understand the structure of a field or help make connections to distant
fields.”
Rumen
Dangovski and Li Jing, the MIT graduate students who conducted the research and
co-authored the journal article, said while this is not the first time AI has
been used to summarize research papers, their approach is novel. They use a
“rotational unit of memory” or RUM to find patterns between words.
The
advantage of the RUM technique, said Dangovski, is that it is able to recall
more information with greater accuracy than other approaches. RUM was
originally developed for use in physics research, for example, to explore the
behavior of light in complex materials, but it works well for natural language
processing, he said. The team also believes the technique could be used to
improve computer speech recognition and machine translation -- where computers
generate translations of speech or text from one language to another.
Using
RUM, the scientists were able to generate the following summary of research
into raccoon roundworm infections: "Urban
raccoons may infect people more than previously assumed. Seven percent of
surveyed individuals tested positive for raccoon roundworm antibodies. Over
90 percent of raccoons in Santa Barbara play host to this parasite."
The
RUM summary was easier to read than one generated using a more established
technique called long short-term memory (LSTM), which looked like this:
"Baylisascariasis, kills mice, has endangered the allegheny woodrat and
has caused disease like blindness or severe consequences. This infection,
termed ‘baylisascariasis,’ kills mice, has endangered the allegheny woodrat and
has caused disease like blindness or severe consequences. This infection,
termed ‘baylisascariasis,’ kills mice, has endangered the allegheny
woodrat."
An
illustration of how researchers used AI technology to summarize their own
research paper. Animation credit: Tsveta Tomova
Summarization
might save scientists time, but it is not effective in helping scientists
identify new targets for research, said Costas Bekas, manager of the
Foundations of Cognitive Computing group at IBM-Research Zurich.
Bekas’s
team is developing what they call “cognitive discovery” tools, which extract knowledge
not only from the text of research papers but also from the images and graphs
within them. So far, the team has built search engines in the fields of
chemistry, pharmaceuticals and materials science.
Instead
of taking months to perform a literature review, Bekas hopes the technology
could reduce the time frame significantly. The technology could help scientists
quickly understand where knowledge gaps lie, which he said is a new frontier in
research and development.
Charles
Dhanaraj, executive director of the Center for Translational Research in
Business at Temple University's Fox School of Business, believes AI will help
improve the efficacy of research, but notes it is unrealistic to assume that AI
could, for example, read 200 research papers and spit out a perfect one-page
literature review.
“In
reality, you’re going to get a crappy result that you will have to keep
modifying. Each iteration will get better. But by the time you arrive at a
reasonable combination of words and concepts, you may have spent as much time,
if not more, as if you had just done the work yourself,” he said.
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