Tuesday, July 22, 2014
When the Associated Press announced plans to use computers to write corporate earnings stories, a number of journalists asked me if I was as horrified by the prospect as they were. In fact, I think robots could do better than some reporters.
With all respect and affection for my fellow journalists, I have concluded that a well-programmed set of algorithms can be far more analytic and precise than the sorts of harried, math-averse humans who are widely employed to write about complex business matters. Here is a case in point:
Poynter.Org led a story today about Gannett’s second-quarter earnings with a headline saying “Circulation Revenue Rises at Gannett Local Papers.” The problem with the headline is that the press release and the accompanying financial tables provided by the company showed unambiguously that circulation revenues actually FELL in both the first and second quarters of the year.
For the record, as reported in the press release, circulation revenue at Gannett’s newspapers for the first half of the year was down 1%, ad sales revenue was down 5.3% and “all other publishing” revenue was down 2.4%.
So, how did the Poynter reporter get it wrong? Because, in his haste to crank out a story, the author evidently relied on the bafflegab in Gannett’s press release, instead of looking at the several pages of detailed financial tables appended to it. In fairness the writer, who was alerted to this issue but so far has not amended his article, what human wouldn’t be confused by the following statement from the company:
“Circulation revenues were $277.9 million, down just 0.6 percent from $279.7 million in the second quarter in 2013. An increase in circulation revenue at Newsquest [GCI’s division in the United Kingdom] was offset by circulation revenue declines at domestic publishing operations. At local domestic publishing sites, home delivery circulation revenue was up in the quarter due, in part, to strategic pricing actions associated with enhanced content.”
You can’t blame Gannett for trying to put the best face on the umpteenth weak quarter in a row for its publishing operation. And you can sort of see, sort of, how a time-constrained journalist fell into the PR trap by seizing his lede from a fragment of the third sentence in the sixteenth paragraph of the press release. But a well-programmed computer could have done better.
A half-decent, natural-language engine could have assimilated, organized and analyzed the facts and figures provided by the company in far less time that an ordinary human could read, much less unpack the meaning of, the document.
The robot would organize the data into normalized tables for instant publication and then drop the key information into templates designed to produce concise and understandable narratives. Knowing in advance the Wall Street consensus on a company's upcoming earnings, the robot could determine whether the company beat or failed to meet investor expectations. Templates would be pre-programmed with dictionaries that would know a drop in revenue from 2013 to 2014 was, depending on the degree of decline, a dip, a slip, a tumble or a plunge.