Mixture descriptions: Why researchers are suddenly obsessed with elevator pitches.

Why Researchers Are Suddenly Obsessed with Elevator Pitches

Why Researchers Are Suddenly Obsessed with Elevator Pitches

Lexicon Valley
A Blog About Language
Nov. 23 2015 9:00 AM

It’s Out of Africa meets Pretty Woman! The Art and Science of Mixture Descriptions

The Martian.
The Martian: Apollo 13 meets Castaway

Photo by Aidan Monaghan. Image courtesy of Twentieth Century Fox Film Corp.

Capturing the essence of a movie is tricky work, especially when you’ve only got a few minutes to pitch your idea in front of a high-powered executive. It’s Out of Africa meets Pretty Woman! Ghost meets The Manchurian Candidate! These classic “elevator pitches” are examples of “mixture descriptions”: characterizations that liken a target document to blends of other documents. Mixture descriptions also appear on Amazon reviews, which prize brevity and limpidity. Because they do such a beautiful job distilling and transmitting nuanced information, researchers have begun to examine them more closely

Katy Waldman Katy Waldman

Katy Waldman is a Slate staff writer.

According to library scientists Peter Organisciak and Michael Twidale, a typical mixture description can have up to three parts. There’s the mixture itself, which frequently names something a “cross between,” “a mashup of,” a “combination of,” or “the offspring of” X and Y. (“Empire is a mashup of King Lear and Hustle and Flow.”) Sometimes, there’s also a qualifier—“Empire is a better [mixture]” or a [mixture] without the charm”—meant to “subjectively realign a listener’s expectations.” Finally, there can be a twist, or a modifier that offers further thematic or stylistic adjustments: “…in space.”

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Voilà. Empire is a much better cross between Glee and As the World Turns, but with black people.

In a paper for IDEALS (the Illinois Digital Environment for Access to Learning and Scholarship), Organisciak and Twidale comb the Web for mixture descriptions and contrast them with traditional IMDB summaries. The film Looper, for instance, gets this official recap: “In 2074, when the mob wants to get rid of someone, the target is sent into the past, where a hired gun awaits—someone like Joe—who one day learns the mob wants to ‘close the loop’ by sending back Joe’s future self for assassination.” From a lay reviewer, here’s the mixture description: “12 Monkeys meets The Terminator.” This tag, Organisciak and Twidale argue, gets the point across more efficiently, elegantly, and vividly. It does what Howard Nemerov once said a well-wrought metaphor should do: produce an uncanny moment of recognition as unlikely terms are sparked against each other.

“Such phrases are very lucid for those unfamiliar with the described target, while often offering an ‘a-ha’ element for those that are,” they write. That flash of instantaneous comprehension, wired into a minimal number of words, makes mixture descriptions of special interest to the people whose job it is to catalog works according to their defining or governing essence, aka librarians. In fact, there exists an entire field of library science called “subject analysis,” which concerns itself with the study of “aboutness” and how to crystallize documents into information-rich taglines. Here are two fundamental theorems of aboutness:

If document A is about X and document B is about X, then documents A and B can be said to be about X.
If document A is about X and document A is about Y, then document A can be said to be about X and Y.

In these axiomatic statements, X and Y are known as “information carriers” or “information components” (ICs). A and B are “information objects” (IOs). A useful description accurately and succinctly conveys when an IO contains an IC. In its strongest form, it alerts you that an IC plays a central role in the IO’s aboutness. You can think of the act of description as tying a document to one or more governing abstractions, whether it’s “coming of age story” or “space odyssey” or “adult musical.” When Netflix serves up ludicrously specific categories like “gangster rom-coms” and “dark superhero reboots involving animals,” it is getting creative with its ICs, smushing them together and placing the target object in their intersection.

That’s sort of like what mixture descriptions do, except that, rather than abstractions or archetypes, they invoke specific films—they use “indirect aboutness.” Instead of saying document A is about X, you say that document A is like document B, which everyone knows is about X. Such aboutness by proxy has several advantages over garden-variety aboutness. It “allows for the communication of latent properties”: ICs you can’t quite articulate but that someone else could still intuit from your comparison. Donnie Darko, for example, has a very peculiar and specific tone. You might try to evoke it with words like “spacey” or “dreamy” or “disturbing,” but it requires much less effort to just say a movie is kinda like Donnie Darko.

Because mixture descriptions require so much interpretation, they are harder than most other cataloging techniques to automate. They’re also a lot more fun to invent. Concocting the perfect genealogy for a new movie (The Martian equals the love child of Castaway and Apollo 13!) is the kind of creative game that people delight in playing; the researchers suggest that librarians might even use that fun to crowdsource some of their filing and tagging. In fact, I should have played it myself; I could have saved you a bit of time by simply explaining that mixture descriptions are what you’d get if you put Mad Libs, Taboo, and information science in a blender. You’d have known what I meant.