Every robot journalist first needs to ingest a bunch of data. Data rich domains like weather were some of the first
to have practical natural language generation systems. Now we’re seeing
a lot of robot journalism applied to sports and finance — domains where
the data can be standardized and made fairly clean. The development of sensor journalism
may provide entirely new troves of data for producing automated
stories. Key here is having clean and comprehensive data, so if you’re
working in a domain that’s still stuck with PDFs or sparse access, the
robots haven’t gotten there yet.
After data is read in by the algorithm the next step is to compute interesting or newsworthy features from the data. Basically the algorithm is trying to figure out the most critical aspects of an event, like a sports game. It has newsworthiness criteria built into its statistics. So for example, it looks for surprising statistical deviations like minimums, maximums, or outliers, big swings and changes in a value, violations of an expectation, a threshold being crossed, or a substantial change in a predictive model. “Any feature the value of which deviates significantly from prior expectation, whether the source of that expectation is due to a local computation or from an external source, is interesting by virtue of that deviation from expectation,” the Narrative Science patent reads. So for a baseball game the algorithm computes “win probability” after every play. If win probability has a big delta in-between two plays it probably means something important just happened and the algorithm puts that on a list of events that might be worthy of inclusion in the final story.
Once some interesting features have been identified, angles are then selected from a pre-authored library. Angles are explanatory or narrative structures that provide coherence to the overall story. Basically they are patterns of events, circumstances, entities, and their features. An angle for a sports story might be “back-and-forth horserace”, “heroic individual performance”, “strong team effort”, or “came out of a slump”. Certain angles are triggered according to the presence of certain derived features (from the previous step). Each angle is given an importance value from 1 to 10 which is then used to rank that angle against all of the other proposed angles.
After data is read in by the algorithm the next step is to compute interesting or newsworthy features from the data. Basically the algorithm is trying to figure out the most critical aspects of an event, like a sports game. It has newsworthiness criteria built into its statistics. So for example, it looks for surprising statistical deviations like minimums, maximums, or outliers, big swings and changes in a value, violations of an expectation, a threshold being crossed, or a substantial change in a predictive model. “Any feature the value of which deviates significantly from prior expectation, whether the source of that expectation is due to a local computation or from an external source, is interesting by virtue of that deviation from expectation,” the Narrative Science patent reads. So for a baseball game the algorithm computes “win probability” after every play. If win probability has a big delta in-between two plays it probably means something important just happened and the algorithm puts that on a list of events that might be worthy of inclusion in the final story.
Once some interesting features have been identified, angles are then selected from a pre-authored library. Angles are explanatory or narrative structures that provide coherence to the overall story. Basically they are patterns of events, circumstances, entities, and their features. An angle for a sports story might be “back-and-forth horserace”, “heroic individual performance”, “strong team effort”, or “came out of a slump”. Certain angles are triggered according to the presence of certain derived features (from the previous step). Each angle is given an importance value from 1 to 10 which is then used to rank that angle against all of the other proposed angles.
Once the angles have been determined and ordered they are linked to specific story points,
which connect back to individual pieces of data like names of players
or specific numeric values like score. Story points can also be chosen
and prioritized to account for personal interests such as home team
players. These points can then be augmented with additional factual
content drawn from internet databases such as where a player is from, or
a quote or picture of them.
The last step the robot journalist takes is natural language generation, which for the Narrative Science system is done by recursively traversing all of the angle and story point representations and using phrasal generation routines to generate and splice together the actual English text. This is probably by far the most straightforward aspect of the entire pipeline — it’s pretty much just fancy templates.
So, there you have it, the pipeline for a robot journalist:
(1) ingest data, (2) compute newsworthy aspects of the data, (3) identify relevant angles and prioritize them, (4) link angles to story points, and (5) generate the output text.
- The Anatomy of a Robot Journalist
The last step the robot journalist takes is natural language generation, which for the Narrative Science system is done by recursively traversing all of the angle and story point representations and using phrasal generation routines to generate and splice together the actual English text. This is probably by far the most straightforward aspect of the entire pipeline — it’s pretty much just fancy templates.
So, there you have it, the pipeline for a robot journalist:
(1) ingest data, (2) compute newsworthy aspects of the data, (3) identify relevant angles and prioritize them, (4) link angles to story points, and (5) generate the output text.
- The Anatomy of a Robot Journalist
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