Aspect-Driven Multi-Document Summarization via RNN and Attention
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Multi-document summarization is a resource-intensive task due to diverse, redundant, and noisy information within documents, and existing methods fail to effectively consider sequential context information and relationships between documents, leading to suboptimal summaries.
Innovation Solution
The approach generates document representations using a recurrent neural network (RNN) encoder for sentence embeddings, calculates aspect saliences through an attention mechanism, and combines these with initial salience scores to produce a summary that considers both sequential context and document relationships, incorporating secondary data like readers' comments to identify attractive information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multi-document summarization is performed using traditional methods, then the task can be completed, but the quality of summaries is suboptimal due to failure to consider sequential context information and relationships between documents
Solution Approach 1:
The patent segments the summarization task into distinct components: sequential context modeling within documents using RNNs, document relationship modeling using graph networks, and aspect-based information extraction. This segmentation allows each component to be optimized independently while maintaining overall system coherence, resolving the contradiction between summary quality and system complexity.
Solution Approach 2:
The patent employs a nested architecture where RNN-based document representations are embedded within graph network structures that model document relationships, which in turn are integrated into an aspect-based summarization framework. This nested doll approach enables multiple levels of context modeling without linearly increasing complexity, as each layer builds upon and utilizes the outputs of previous layers.
2Loss of information
If documents with diverse, redundant, and noisy information are processed, then comprehensive coverage is achieved, but resource consumption increases significantly
Solution Approach 1:
The patent extracts only the most salient and non-redundant information from diverse documents by using attention mechanisms to identify key aspects and sentences. Rather than processing all information equally, the system extracts relevant information based on aspect saliences and sentence importance scores, reducing computational resources needed while maintaining comprehensive coverage of essential content.
Solution Approach 2:
The patent dynamically adjusts processing parameters based on document characteristics, using learned salience scores to prioritize certain documents or sentences over others. This parameter change approach allows the system to adapt computational resources to the actual information density and importance of different document segments, achieving comprehensive coverage with reduced resource consumption.
3Measurement precision
If sequential context information and document relationships are fully considered, then summary quality improves, but processing time increases
Solution Approach 1:
The patent performs preliminary encoding of individual documents using RNNs to capture sequential context before introducing them into the graph network for relationship modeling. This preliminary action pre-processes and compresses document information into fixed-length representations, enabling subsequent relationship modeling to proceed more efficiently without sacrificing the quality of sequential context capture.
Solution Approach 2:
The patent maintains continuous information flow through the pipeline by using the output representations from sequential modeling directly as inputs to relationship modeling, and subsequently to aspect-based summarization. This continuity eliminates redundant processing steps and ensures that useful contextual information is preserved and propagated throughout the entire summarization process, improving quality without proportional increases in processing time.
Data Source
AI summary
Methods, systems, and computer-readable storage media for generating document representations of documents in a set of documents based on sentence embeddings that are provided using a recurrent neural network (RNN) encoder, each document including an electronic document, generating aspect representations based on sentences included in documents of the set of documents, and comment documents in a set of comment documents, determining a first topic representation based on the document representations, determining a second topic representation based on aspect saliences with respect to the first topic representation, calculating salience scores, each salience score associated with a respective sentence, and calculated based on a set of initial salience scores, and a respective aspect salience score, and generating a summary of the set of documents based on the salience scores, the summary including one or more sentences included in documents of the set of documents.