TECHNICAL FIELD

The present disclosure relates to the field of natural language generation (NLG). Particularly, but not exclusively, the present disclosure relates to a system and method for generating abstractive summary.

BACKGROUND

Generally, abstractive summary generation pertains to summarization of information provided in a document. The abstractive summary retains the necessary information from the document. The document summarization techniques create a representative summary or an abstract of the entire document by retrieving the most informative sentences in the document. For generating abstractive summaries, the retrieved sentences are represented semantically, and a natural language generation technique is used to create abstractive summary.

An issue with the existing techniques is the lack of ability to retrieve the one or more sentences from a document based on the semantic graph and graph index data.

An issue with the existing techniques is the lack of ability to identify and remove multiple sentences conveying same information for generating abstractive summary. Hence, in the existing techniques, although summary is generated, the summary may not provide clarity of information required by a user.

The information disclosed in this background of the disclosure section is only for enhancement of understanding of the general background of the invention and should not be taken as an acknowledgement or any form of suggestion that this information forms the prior art already known to a person skilled in the art.

SUMMARY

One or more shortcomings of the prior art are overcome, and additional advantages are provided through the provision of method of the present disclosure.

Additional features and advantages are realized through the techniques of the present disclosure. Other embodiments and aspects of the disclosure are described in detail herein and are considered a part of the claimed disclosure.

Disclosed herein is a method for generating abstractive summary. In an embodiment, the method includes receiving a query for generating an abstractive summary from a document among a plurality of documents stored in a document database and splitting the query into one or more lexical units. Further, the method includes generating a semantic graph and a graph index based on a role assigned to the one or more lexical units in the query. Furthermore, the method includes determining a measure of information of retrieved one or more sentences from the document, wherein the one or more sentences having a semantic graph analogous to the generated semantic graph of the query are retrieved from the document. Finally, the method includes performing at least one of re-ordering and re-phrasing of at least one of the retrieved one or more sentences based on the computed measure of information and the one or more lexical units in the retrieved one or more sentences to generate the abstractive summary.

Further, the present disclosure discloses a summary generation unit, for generating abstractive summary. In an embodiment, the summary generation unit includes a processor and a memory communicatively coupled to the processor, wherein the memory stores the processor executable instructions, which, on execution, causes the processor to receive a query for generating the abstractive summary from a document among a plurality of documents stored in a document database and splitting the query into one or more lexical units. Further, the processor is configured to generate a semantic graph and a graph index based on a role assigned to the one or more lexical units in the query. Furthermore, the processor is configured to determine a measure of information of retrieved one or more sentences from the document, wherein the one or more sentences having a semantic graph analogous to the generated semantic graph of the query are retrieved from the document. Finally, the processor is configured to perform at least one of re-ordering and re-phrasing of at least one of the retrieved one or more sentences based on the computed measure of information and the one or more lexical units in the retrieved one or more sentences to generate the abstractive summary.

The foregoing summary is illustrative only and is not intended to be in any way limiting. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features may become apparent by reference to the drawings and the following detailed description.

