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I'm relatively new in the field of Information Extraction and was wondering if there are any methods to summarize multiple headlines on the same topic, like some kind of "average" of headlines. Imagine like 20 headlines from news articles on the topic that the Los Angeles Rams won the Super Bowl such as "Rams win Super Bowl", "Los Angeles rallies past Bengals to win Super Bowl", ...

The goal would be to find one "average" sentence that summarizes these headlines. I already searched in Google and Google Scholar, but I don't find anything that's fit, so I'm not sure if there is actually nothing or if I just don't know the right method/keyword to serach here.

Thank you in advance!

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  • $\begingroup$ In your example, is "Rams win Super Bowl" the solution? i.e. the simplest summary? Otherwise, what do you mean by "average" sentence? Give us an example. I think the simplest summary is easier and more useful to get. $\endgroup$
    – smci
    Commented Mar 8, 2022 at 19:15
  • $\begingroup$ Yeah Rams win Super Bowl would be a solution. I know that there is no such thing as an average of text data, but i like your term "simplest summary". Kind of like a correct English sentences preferably short that contains the most important information. Probably a very very simple version would count the words and use the most frequent one to make a sentence, I know far from perfect, so maybe there is something bit more advanced. $\endgroup$
    – LaLeLo
    Commented Mar 8, 2022 at 21:12

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The closest NLP task that I can think of is automatic summarization: given some text of any length, the system is supposed to produce a short summary of the most important points.

I would imagine that if one provides multiple similar headlines to a good summarization system, the system should be able to output only the main information as output. It's not guaranteed that it would be exactly one sentence though.

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  • $\begingroup$ Thank you that is defintely a god starting point for me to take a look into :) $\endgroup$
    – LaLeLo
    Commented Mar 9, 2022 at 7:09

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