Salience-Aware Cross-Attention for Adaptive Abstractive Summarization
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Solution Overview
Problem
Existing abstractive summarization models rely on imperfect extractive summaries as guidance, leading to information loss, noise, and positional or length biases, especially when dealing with documents of varying abstractiveness.
Innovation Solution
The proposed method, SEASON, uses salience allocation expectation to guide abstractive summarization, incorporating a salience-aware cross-attention (SACA) decoder that adapts to different documents by mapping salience degrees to trainable embeddings and determining the appropriate signal to accept from the salience guidance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If extractive summaries are used as guidance for abstractive summarization, then the model gains higher-level understanding of input documents, but information loss and noise occur when there are too many or too few summary-worthy sentences
Solution Approach 1:
The patent implements dynamic salience allocation that adapts to each document's characteristics. The model learns to allocate different numbers of salient sentences based on document properties rather than using a fixed extractive summary length. This dynamic approach allows the guidance to remain reliable whether the document has many or few summary-worthy sentences, preventing both information loss and noise issues.
Solution Approach 2:
The patent changes the parameter of salience allocation from fixed (extractive summary length) to variable. By learning to allocate salient sentences dynamically based on document characteristics, the system adjusts the number and selection of guidance sentences to match each document's abstractiveness, thereby maintaining guidance reliability without causing information loss or noise.
2Reliability
If extractive summaries are used as guidance, then abstractive summarization performance improves in some cases, but the approach is not flexible to adapt to documents with different abstractiveness
Solution Approach 1:
The system employs dynamic salience allocation that automatically adapts to documents with varying abstractiveness. The model learns to allocate salient sentences based on document characteristics, making the guidance flexible rather than fixed. This enables the system to maintain high summarization performance across documents with different levels of abstractiveness.
Solution Approach 2:
The patent creates a universal salience allocation mechanism that works across documents with different abstractiveness levels. The learned allocation strategy serves multiple functions: it adapts to varying document properties, maintains performance consistency, and provides flexible guidance regardless of whether documents are highly abstract or concrete.
3Ease of operation
If extractive summaries are used as guidance, then the model receives structured input, but positional biases and length biases are introduced due to imperfect selection processes
Solution Approach 1:
The patent implements a learned salience allocation process that uses feedback to identify and correct biases. The model learns from training data to allocate salient sentences in a way that minimizes positional and length biases. This feedback mechanism ensures that the structured input provided to the model remains fair and reliable, preventing systematic errors from propagating through the summarization process.
Data Source
AI summary
A method including: receiving an input comprising natural language texts at an encoder; adding a token to the input; obtaining a last-layer hidden state as a natural language text representation; feeding the natural language text representation into a single-layer classification head; predicting a salience allocation based on the single-layer classification head; developing a salience-aware cross-attention (SACA) decoder to determine salience in the natural language text representation; mapping a plurality of salience degrees to a plurality of trainable salience embeddings; estimating an amount of signal to accept from the plurality of trainable salience embeddings; incorporating the salience allocation and the signal in a cross-attention layer model; and generating a summarization based on the SACA decoder and the cross-attention layer model.