Search results summarization tuning
One or more aspects of the method, apparatus, and non-transitory computer readable medium include receiving a query relating to an item and a summarization type indicating an emphasis on item similarities or item differences, obtaining, using a search component, descriptions of items relevant to the query, generating input data for a machine learning model based on the descriptions and the summarization type, and generating, using the machine learning model, a summarization of the descriptions based on the input data in response to the query, wherein the summarization emphasizes the item similarities or item differences based on the summarization type.
1 . A method comprising:
receiving a query relating to an item and a summarization type indicating an emphasis on item similarities or item differences;
obtaining, using a search component, descriptions of items relevant to the query;
generating input data for a machine learning model based on the descriptions and the summarization type; and
generating, using the machine learning model, a summarization of the descriptions based on the input data in response to the query, wherein the summarization emphasizes the item similarities or the item differences based on the summarization type.
2 . The method of claim 1 , further comprising:
tuning the summarization to include topics learned from a user.
3 . The method of claim 1 , further comprising:
processing the query using the machine learning model to identify search terms; and
conducting a search based on the query to identify items relevant to the search terms, wherein descriptions associated with the items are used by the machine learning model to generate questions for a user.
4 . The method of claim 1 , wherein:
the generated summarization is a comparative summarization that suppresses repeated information in the descriptions and accentuates similarities and differences between the items.
5 . The method of claim 1 , wherein:
generating follow-up questions for a user using the machine learning model to identify new search topics.
6 . The method of claim 5 , further comprising:
conducting a subsequent search based on the new search topics.
7 . The method of claim 1 , wherein:
a conciseness of the summarization of the descriptions is based on a compression parameter that determines.
8 . A method, comprising:
receiving a ranked list of items from a search component as a result of an initial search query;
presenting a plurality of ideation questions generated by a machine learning model to a user, wherein the plurality of ideation questions are generated based on the ranked list of items and the initial search query; and
conducting, using the search component, a subsequent search using a new query generated by the machine learning model based on responses provided by the user to the generated ideation questions.
9 . The method of claim 8 , wherein:
the user selects one or more ideation questions from the plurality of ideation questions to further refine a search space.
10 . The method of claim 8 , wherein:
the plurality of ideation questions are generated based on topics identified by the user through the responses provided by the user.
11 . The method of claim 8 , further comprising:
optimizing a distribution of the plurality of generated ideation questions using a multiarmed bandit approach.
12 . The method of claim 8 , further comprising:
identifying topics from the responses provided by the user to the plurality of generated ideation questions; and
incorporating the identified topics into the new query.
13 . The method of claim 12 , further comprising:
conducting the subsequent search using the new query generated based on the identified topics.
14 . The method of claim 8 , further comprising:
aggregating descriptive content for the ranked list of items; and
providing the aggregated descriptive content to a summarization component as an input to generate the ideation questions.
15 . A system comprising:
one or more processors;
one or more memories including instructions executable by the one or more processors to:
receive an initial search query relating to an item and a summarization type indicating an emphasis on item similarities or item differences;
obtain, using a search component, a ranked list of items, as a result of the initial search query;
generate, using a machine learning model, a comparative summarization of descriptions for the ranked list of items based on the summarization type; and
present the comparative summarization and a plurality of generated ideation questions to a user, wherein the plurality of ideation questions are generated by an ideation component based on the ranked list of items and the initial search query.
16 . The system of claim 15 , wherein:
the machine learning model includes a transformer encoder and a transformer decoder.
17 . The system of claim 15 , wherein:
the machine learning model is a pre-trained large language model (LLM) configured to generate the comparative summarization of descriptions based on a vector embedding.
18 . The system of claim 15 , further comprising:
receiving, by the machine learning model, responses to the plurality of ideation questions from the user; and
conducting a subsequent search using a new query generated by the machine learning model based on the responses.
19 . The system of claim 18 , wherein:
the ideation component utilizes a multi-armed bandit approach to identify the plurality of generated ideation questions for presentation to the user from a larger set of generated ideation questions.
20 . The system of claim 19 , wherein:
the machine learning model is trained based on supervised contrastive learning.