Extractive Summarization via Weighted Dissimilarity and Importance Aligned Key Iterative Algorithm
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 .
Code (1)
Tasks
Extractive SummarizationSimilar Papers 제목 키워드 기반
SumTitles: a Summarization Dataset with Low Extractiveness
The existing dialogue summarization corpora are significantly extractive. We introduce a methodology for dataset extractiveness evaluation and present a new low-extractive corpus of movie dialogues for abstractive text s…
Abstractive Text SummarizationText SummarizationA Redundancy-Aware Sentence Regression Framework for Extractive Summarization
Existing sentence regression methods for extractive summarization usually model sentence importance and redundancy in two separate processes. They first evaluate the importance f(s) of each sentence s and then select sen…
Document SummarizationExtractive SummarizationMulti-Document Summarizationregression+1Demystifying Multi-Faceted Video Summarization: Tradeoff Between Diversity,Representation, Coverage and Importance
This paper addresses automatic summarization of videos in a unified manner. In particular, we propose a framework for multi-faceted summarization for extractive, query base and entity summarization (summarization at the …
DiversityVideo SummarizationMulti-layered graph-based multi-document summarization model
Multi-document summarization is a process of automatic generation of a compressed version of the given collection of documents. Recently, the graph-based models and ranking algorithms have been actively investigated by t…
Document SummarizationExtractive Document SummarizationExtractive Text Summarizationmodel+3Fair Summarization: Bridging Quality and Diversity in Extractive Summaries
Fairness in multi-document summarization of user-generated content remains a critical challenge in natural language processing (NLP). Existing summarization methods often fail to ensure equitable representation across di…
DiversityDocument SummarizationExtractive SummarizationFairness+1