Extractive Summarization via Weighted Dissimilarity and Importance Aligned Key Iterative Algorithm
This work addresses the problem of efficient and accurate summarization for applications like customer review analysis, though it appears incremental as it builds on conventional methods with a novel optimization approach.
The paper tackles extractive summarization by introducing an algorithm that maximizes weighted dissimilarity to produce summaries that are both representative and non-redundant, achieving computational complexity of O(SNlogN) and benchmark scores comparable to human and existing algorithms on customer review data.
We present importance aligned key iterative algorithm for extractive summarization that is faster than conventional algorithms keeping its accuracy. The computational complexity of our algorithm is O($SNlogN$) to summarize original $N$ sentences into final $S$ sentences. Our algorithm maximizes the weighted dissimilarity defined by the product of importance and cosine dissimilarity so that the summary represents the document and at the same time the sentences of the summary are not similar to each other. The weighted dissimilarity is heuristically maximized by iterative greedy search and binary search to the sentences ordered by importance. We finally show a benchmark score based on summarization of customer reviews of products, which highlights the quality of our algorithm comparable to human and existing algorithms. We provide the source code of our algorithm on github https://github.com/qhapaq-49/imakita .