Abstractive Summarization Using Hierarchical Section-Level Representations
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Solution Overview
Problem
Current abstractive summarization methods are inadequate for long form documents, such as scientific or enterprise articles, as they fail to effectively capture important points and often suffer from repetition and lower frequency term resolution.
Innovation Solution
A document summarization network utilizing a general encoder-decoder architecture with attentive decoding, incorporating word-level and section-level representations, and leveraging neural attention mechanisms to generate summaries that consider both word-level and section-level information, while addressing the exploding/vanishing gradients problem using LSTMs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing abstractive summarization methods are used on long form documents, then the summarization task can be performed, but the important points are not effectively captured and repetition occurs
Solution Approach 1:
The patent segments the document into multiple sections and processes each section independently through separate encoder networks, then aggregates the section-level representations. This segmentation allows the model to focus on and capture important points in each section without being overwhelmed by the entire long document, thereby improving summarization accuracy and reducing repetition.
Solution Approach 2:
The patent transitions from word-level representations to section-level representations by adding a hierarchical dimension. The encoder networks process words within sections and generate section-level embeddings that capture the semantic meaning of entire sections, enabling the model to operate at multiple levels of abstraction and improve overall summarization quality.
2Device complexity
If standard RNNs are used for document summarization, then the model structure is simple, but the exploding/vanishing gradients problem occurs
Solution Approach 1:
The patent replaces standard RNNs with LSTM (Long Short-Term Memory) networks, which use a different mechanical structure with gates (forget gate, input gate, output gate) to control information flow. This substitution resolves the exploding/vanishing gradients problem by allowing selective retention and forgetting of information over time, enabling effective training on long documents.
3Productivity
If existing summarization methods are applied to long documents, then processing can be performed, but repetition and lower frequency term resolution occur
Solution Approach 1:
The patent applies local quality by processing each document section through dedicated encoder networks that generate section-specific representations. This allows the model to pay attention to and resolve terms differently in different sections, improving term resolution precision while maintaining efficient processing through parallel section encoding.
Data Source
AI summary
Techniques are disclosed for abstractive summarization process for summarizing documents, including long documents. A document is encoded using an encoder-decoder architecture with attentive decoding. In particular, an encoder for modeling documents generates both word-level and section-level representations of a document. A discourse-aware decoder then captures the information flow from all discourse sections of a document. In order to extend the robustness of the generated summarization, a neural attention mechanism considers both word-level as well as section-level representations of a document. The neural attention mechanism may utilize a set of weights that are applied to the word-level representations and section-level representations.