In the realm of patent document summarization,
employing artificial neural networks offers a powerful
tool, but its effectiveness relies heavily on a systematic
approach. At the outset, it is paramount to delineate the objectives
clearly. Understanding the nuanced structure of patent documents,
encompassing sections like claims, abstracts, and descriptions, is
fundamental. This foundational comprehension informs subsequent
stages.
The crux of the systematic approach lies in the neural network’s
design. Implementing an encoder-decoder architecture, often with
LSTM or GRU units, facilitates sequence-to-sequence learning.
Incorporating an attention mechanism enables the model to focus
on specific segments of the input text, enriching the quality of the
generated summary. Crucially, user preferences must be integrated
into the learning process. Defining these preferences within the loss
function is key, potentially through reinforcement learning paradigms,
where the model is incentivized based on adherence to these defined
criteria.
The training phase involves the crafting of a custom loss
function that amalgamates traditional sequence-to-sequence loss with
preference-based metrics. Fine-tuning various hyper parameters such
as architecture specifics, learning rates,...