Labeled Clustering Preprocessing to Meet NLP Input Constraints

The patent applies preliminary action by implementing a preprocessing pipeline that performs labeled clustering and N-gram analysis before the main summarization process. This preliminary processing identifies and removes non-relevant sentences in advance, reducing the document size to meet input constraints of NLP models and improving overall processing efficiency for large volumes of text.

The patent segments the document processing into multiple stages: labeled clustering to identify relevant vs non-relevant sentences, N-gram analysis to further filter sentences, and final summarization. This segmentation allows efficient handling of large documents by processing them in manageable chunks through different filtering mechanisms.

The preprocessing pipeline performs preliminary labeled clustering and N-gram analysis to ensure the output consistently meets input constraints of NLP models. This preliminary action filters out non-relevant content before the main summarization, guaranteeing compliance with model input requirements.

The system incorporates feedback mechanisms where the preprocessing pipeline adjusts its filtering based on the requirements of downstream NLP models. The labeled clustering and N-gram analysis continuously refine the document reduction to ensure reliable compliance with input constraints.

The preprocessing pipeline is segmented into distinct modules: labeled clustering module for initial sentence classification, N-gram analysis module for further filtering, and integration layer that combines results. This segmentation manages complexity by organizing operations into manageable, independent components.

The patent merges labeled clustering and N-gram analysis into a unified preprocessing pipeline that operates sequentially on the same document data. This combining approach leverages the strengths of both techniques while managing overall complexity through integrated processing rather than separate independent systems.