Abstractive Summarization via Local Attention Neural Model
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Solution Overview
Problem
Current summarization systems, particularly in natural language understanding, face challenges in producing abstractive summaries that capture the core meaning of input text, as they often rely on extractive approaches that limit creativity and flexibility in generating condensed representations.
Innovation Solution
A fully data-driven approach using a local attention-based model that generates each word of the summary conditioned on the input sentence, combining a neural language model with a contextual input encoder and incorporating an attention-based encoder to learn a latent soft alignment, allowing for abstractive summarization that can generalize and paraphrase.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If extractive approaches are used for summarization, then the system is simpler to implement, but the creativity and flexibility in generating condensed representations are limited
Solution Approach 1:
The patent replaces traditional extractive summarization methods with a neural language model-based abstractive summarization system. This substitution enables the system to generate novel summaries through learned patterns rather than simply selecting and copying existing text segments, thereby achieving both creativity and implementation feasibility through end-to-end training
Solution Approach 2:
The patent changes the fundamental parameter of summarization from extractive selection to abstractive generation using neural networks. By adjusting the model architecture parameters including attention mechanisms and contextual encoding, the system achieves flexible and creative summarization while maintaining implementation simplicity through unified training objectives
2Adaptability or versatility
If abstractive summarization is implemented, then creativity and flexibility improve, but the model complexity increases
Solution Approach 1:
The patent segments the abstractive summarization task into distinct functional components: contextual input encoding, attention-based word generation, and sequence-to-sequence mapping. This segmentation allows each component to be optimized independently while maintaining overall model manageability and enabling creative summarization through coordinated interaction of modules
Solution Approach 2:
The patent introduces an attention mechanism as an intermediary between the input text and output summary generation. This intermediary enables the model to selectively focus on relevant input segments when generating each output word, managing complexity through localized attention weights rather than processing the entire input sequence uniformly
3Productivity
If local attention-based model is used, then scalability to large datasets improves, but the structural complexity of the model increases
Solution Approach 1:
The patent performs preliminary encoding of the input sentence into contextual representations before the summarization generation process. This preliminary action pre-processes the input data into a form that facilitates efficient attention-based word generation, enabling scalability to large datasets by reducing the computational burden during the main generation phase
Solution Approach 2:
The patent implements dynamic attention mechanisms that adaptively weight different input segments based on their relevance to each output word being generated. This dynamic approach allows the model to focus computational resources on the most relevant portions of the input, achieving scalability while managing structural complexity through conditional computation
Data Source
AI summary
In one embodiment, a sequence of input words is received. Each of the input words is encoded as an indicator vector, wherein a sequence of the indicator vectors captures features of the sequence of input words. The sequence of the indicator vectors is then mapped to a distribution of a contextual probability of a first output word in a sequence of output words. For each subsequent output word, the sequence of the indicator vectors is encoded with a context, wherein the context comprises a previously mapped contextual probability distribution of a fixed window of previous output words; and the encoded sequence of the indicator vectors and the context is mapped to the distribution of the contextual probability of the subsequent output word. Finally, a condensed summary is generated using a decoder by maximizing the contextual probability of each of the output words.