Summarization of group chat threads
Systems and methods provide for automated messaging summarization and ranking. The systems and methods may use an integrated machine learning model to perform thread detection, thread summarization, and summarization ranking. The messages may be received from a team chat application, organized, summarized and ranked by the machine learning model, and the results may be returned to the team chat application. In some cases, the ranking may be different for different users of the team chat application.
1. A method for communication technology, comprising:
categorizing a set of messages into a plurality of message threads;
generating a thread summary for each of the plurality of message threads;
ordering the thread summaries; and
transmitting the ordered thread summaries to one or more users
wherein the categorization comprises:
calculating a feature vector for a new message of the set of messages;
calculating a candidate vector for each of a set of existing message threads and for an empty thread;
comparing the feature value to each of the candidate vectors; and
categorizing the new message into the one of the existing message threads or the empty thread based on the comparison.
2. The method of claim 1 , further comprising:
determining that the match value is closest to the candidate value for the empty thread, wherein the new message is categorized into the empty thread based on the determination; and
generating a new empty thread based on the determination.
3. The method of claim 1 , wherein:
the candidate vectors are calculated based on a single direction long short-term memory (LSTM) model.
4. The method of claim 1 , wherein:
the feature vector includes word count information, vocabulary information, message author information, message time information, or any combination thereof.
5. The method of claim 1 , further comprising:
encoding each of the plurality of message threads to produce an encoded thread vector; and
decoding the encoded thread vector to produce the thread summary for each of the plurality of message threads.
6. The method of claim 5 , wherein:
the encoding and decoding are based on an LSTM model, a gated recurrent unit (GRU) model, a transformer model, or any combination thereof.
7. The method of claim 1 , further comprising:
identifying a set of rank orderings for the plurality of message threads; and
generating a ranking value for each of the set of rank orderings, wherein the ordering is based on the ranking values.
8. The method of claim 7 , further comprising:
identifying user information for each of the one or more users; and
selecting a plurality of rank orderings from the set of rank orderings based on the user information, wherein one of the plurality of rank orderings is selected for each of the one or more users.
9. The method of claim 1 , further comprising:
receiving the set of messages from a team chat application, wherein the ordered thread summaries are transmitted to the one or more users via the team chat application.
10. The method of claim 1 , further comprising:
training an integrated machine learning model using a set of training messages and a target set of ranked summaries, wherein categorizing the set of messages, generating the thread summaries, and ordering the thread summaries are all performed using the integrated machine learning model.
11. An apparatus for summarization of group chat threads, comprising: a processor and a memory storing instructions and in electronic communication with the processor, the processor being configured to execute the instructions to:
receive a set of messages from a team chat application;
categorize the set of messages into a plurality of message threads;
generate a thread summary for each of the plurality of message threads;
order the thread summaries; and
transmit the ordered thread summaries to one or more users via the team chat application
wherein the categorization comprises:
calculating a feature vector for a new message of the set of messages;
calculating a candidate vector for each of a set of existing message threads and for an empty thread;
comparing the feature value to each of the candidate vectors; and
categorizing the new message into the one of the existing message threads or the empty thread based on the comparison.
12. The apparatus of claim 11 , the processor being further configured to execute the instructions to:
encode each of the plurality of message threads to produce an encoded thread vector; and
decode the encoded thread vector to produce the thread summary for each of the plurality of message threads.
13. The apparatus of claim 11 , the processor being further configured to execute the instructions to:
identify a set of rank orderings for the plurality of message threads; and
generate a ranking value for each of the set of rank orderings, wherein the ordering is based on the ranking values.
14. The apparatus of claim 13 , the processor being further configured to execute the instructions to:
identify user information for each of the one or more users; and
select a plurality of rank orderings from the set of rank orderings based on the user information, wherein one of the plurality of rank orderings is selected for each of the one or more users.
15. A non-transitory computer readable medium storing code for summarization of group chat threads, the code comprising instructions executable by a processor to:
train an integrated machine learning model using a set of training messages and a target set of ranked summaries;
categorize a set of messages into a plurality of message threads using the integrated machine learning model;
generate a thread summary for each of the plurality of message threads using the integrated machine learning model;
order the thread summaries using the integrated machine learning model; and
transmit the ordered thread summaries to one or more users
wherein the categorization comprises:
calculating a feature vector for a new message of the set of messages;
calculating a candidate vector for each of a set of existing message threads and for an empty thread;
comparing the feature value to each of the candidate vectors; and
categorizing the new message into the one of the existing message threads or the empty thread based on the comparison.
16. The non-transitory computer readable medium of claim 15 , the code further comprising instructions executable by the processor to:
encode each of the plurality of message threads to produce an encoded thread vector; and
decode the encoded thread vector to produce the thread summary for each of the plurality of message threads.
17. The non-transitory computer readable medium of claim 15 , the code further comprising instructions executable by the processor to:
identify a set of rank orderings for the plurality of message threads; and
generate a ranking value for each of the set of rank orderings, wherein the ordering is based on the ranking values.