Who has time to read every article they see shared on Twitter or Facebook, or every document that’s relevant to their job? As information overload grows ever worse, computers may become our only hope for handling a growing deluge of documents. And it may become routine to rely on a machine to analyze and paraphrase articles, research papers, and other text for you.
An algorithm developed by researchers at Salesforce shows how computers may eventually take on the job of summarizing documents. It uses several machine-learning tricks to produce surprisingly coherent and accurate snippets of text from longer pieces. And while it isn’t yet as good as a person, it hints at how condensing text could eventually become automated.
The algorithm produced, for instance, the following summary of a recent New York Times article about Facebook trying to combat fake news ahead of the U.K.’s upcoming election:
The Salesforce algorithm is dramatically better than anything developed previously, according to a common software tool for measuring the accuracy of text summaries.
“I don’t think I’ve ever seen such a large improvement in any [natural-language-processing] task,” says Richard Socher, chief scientist at Salesforce. Socher is a prominent name in machine learning and natural-language processing, and his startup, MetaMind, was acquired by Salesforce in 2016.
The software is still a long way from matching a human’s ability to capture the essence of document text, and other summaries it produces are sloppier and less coherent. Indeed, summarizing text perfectly would require genuine intelligence, including commonsense knowledge and a mastery of language.
Parsing language remains one of the grand challenges of Artificial Intelligence (see “AI’s Language Problem”). But it’s a challenge with enormous commercial potential. Even limited linguistic intelligence—the ability to parse spoken or written queries, and to respond in more sophisticated and coherent ways—could transform personal computing. In many specialist fields—like medicine, scientific research, and law—condensing information and extracting insights could have huge commercial benefits.
Caiming Xiong, a research scientist at Salesforce who contributed to the work, says his team’s algorithm, while imperfect, could summarize daily news articles, or provide a synopsis of customer e-mails. The latter could be especially useful for Salesforce’s own platform.
