Using Statistical and Semantic Models for Multi-Document Summarization

TL;DR

This paper explores combining statistical and semantic models for extractive multi-document summarization, demonstrating that weighted integration and pre-trained vectors improve summary quality on benchmark datasets.

Contribution

It introduces a novel approach of tuning weights between statistical and semantic models, including new semantic models based on GloVe and InferSent, to enhance summarization performance.

Findings

Semantic models improve context understanding over statistical models.

Weighted combination of models yields significant ROUGE score improvements.

Pre-trained vectors further boost summarization accuracy.

Abstract

We report a series of experiments with different semantic models on top of various statistical models for extractive text summarization. Though statistical models may better capture word co-occurrences and distribution around the text, they fail to detect the context and the sense of sentences /words as a whole. Semantic models help us gain better insight into the context of sentences. We show that how tuning weights between different models can help us achieve significant results on various benchmarks. Learning pre-trained vectors used in semantic models further, on given corpus, can give addition spike in performance. Using weighing techniques in between different statistical models too further refines our result. For Statistical models, we have used TF/IDF, TextRAnk, Jaccard/Cosine Similarities. For Semantic Models, we have used WordNet-based Model and proposed two models based on Glove Vectors and Facebook's InferSent. We tested our approach on DUC 2004 dataset, generating 100-word summaries. We have discussed the system, algorithms, analysis and also proposed and tested possible improvements. ROUGE scores were used to compare to other summarizers.