METHODS AND SYSTEMS FOR GENERATING SUMMARIES

1 . A computer-implemented machine learning method for generating real-time summaries, the method comprising:

identifying a speech segment during a conference session;

generating a real-time transcript from the speech segment identified during the conference session;

determining a topic from the real-time transcript generated from the speech segment;

generating a summary of the topic; and

streaming the summary of the topic during the conference session.

2 . The computer-implemented machine learning method of claim 1 , wherein determining the topic from the real-time transcription comprises detecting a drift from the topic to another topic and determining the topic based on the drift.

3 . The computer-implemented machine learning method of claim 2 , wherein detecting the drift comprises detecting based on a pattern of lexical features.

4 . The computer-implemented machine learning method of claim 1 , wherein generating the real-time transcript comprises tagging a speaker identity or a timestamp, and wherein generating the summary of the topic comprises generating the summary using the speaker identity or the timestamp.

5 . The computer-implemented machine learning method of claim 1 , further comprising:

determining another topic from the real-time transcript generated from the speech segment;

determining an irrelevancy of the other topic; and

filtering out the other topic based on the irrelevancy.

6 . The computer-implemented machine learning method of claim 1 , wherein generating the summary of the topic comprises generating an abstractive summary, and wherein streaming the summary of the topic comprises streaming the abstractive summary.

7 . The computer-implemented machine learning method of claim 1 , further comprising:

processing the summary in response to generating the summary, wherein processing comprises adding a speaker identity or a timestamp; and

wherein streaming the summary comprises streaming the summary in response to the processing.

8 . A non-transitory, computer-readable medium storing a set of instructions that, when executed by a processor, cause:

identifying a speech segment during a conference session;

generating a real-time transcript from the speech segment identified during the conference session;

determining a topic from the real-time transcript generated from the speech segment;

generating a summary of the topic; and

streaming the summary of the topic during the conference session.

9 . The non-transitory, computer-readable medium of claim 8 , wherein determining the topic from the real-time transcription comprises detecting a drift from the topic to another topic, and wherein determining the topic comprises determining based on the drift.

10 . The non-transitory, computer-readable medium of claim 9 , wherein detecting the drift comprises detecting based on a pattern of lexical features.

11 . The non-transitory, computer-readable medium of claim 8 , wherein generating the real-time transcript comprises tagging a speaker identity or a timestamp, and wherein generating the summary of the topic comprises generating the summary using the speaker identity or the timestamp.

12 . The non-transitory, computer-readable medium of claim 8 , storing further instructions that, when executed by the processor, cause:

determining another topic from the real-time transcript generated from the speech segment;

determining an irrelevancy of the other topic; and

filtering out the other topic based on the irrelevancy.

13 . The non-transitory, computer-readable medium of claim 8 , wherein generating the summary of the topic comprises generating an abstractive summary, and wherein streaming the summary of the topic comprises streaming the abstractive summary.

14 . The non-transitory, computer-readable medium of claim 8 , storing further instructions that, when executed by the processor, cause:

processing the summary in response to generating the summary, wherein processing comprises adding a speaker identity or a timestamp; and

wherein streaming the summary comprises streaming the summary in response to the processing.

15 . A machine learning system for generating real-time summaries, the system comprising:

a processor;

a memory operatively connected to the processor and storing instructions that, when executed by the processor, cause:

identifying a speech segment during a conference session;

generating a real-time transcript from the speech segment identified during the conference session;

determining a topic from the real-time transcript generated from the speech segment;

generating a summary of the topic; and

streaming the summary of the topic during the conference session.

16 . The machine learning system of claim 15 , wherein determining the topic from the real-time transcription comprises detecting a drift from the topic to another topic and determining the topic based on the drift.

17 . The machine learning system of claim 16 , wherein detecting the drift comprises detecting based on a pattern of lexical features.

18 . The machine learning system of claim 15 , wherein the memory stores further instructions that, when executed by the processor, cause:

determining another topic from the real-time transcript generated from the speech segment;

determining an irrelevancy of the other topic; and

filtering out the other topic based on the irrelevancy.

19 . The machine learning system of claim 15 , wherein generating the summary of the topic comprises generating an abstractive summary, and wherein streaming the summary of the topic comprises streaming the abstractive summary.

20 . The machine learning system of claim 15 , wherein the memory stores further instructions that, when executed by the processor, cause:

processing the summary in response to generating the summary, wherein processing comprises adding a speaker identity or a timestamp; and

wherein streaming the summary comprises streaming the summary in response to the processing.