Opinosis: A Graph Based Approach to Abstractive Summarization of Highly Redundant Opinions
We present a novel graph-based summarization framework (Opinosis) that generates concise abstractive summaries of highly redundant opinions. Evaluation results on summarizing user reviews show that Opinosis summaries have better agreement with human summaries compared to the baseline extractive method. The summaries are readable, reasonably well-formed and are informative enough to convey the major opinions.
Key idea of Opinosis
This paper presents a flexible framework for generating very short abstractive summaries. The key idea is to use a word graph data structure referred to as the Opinosis-Graph to represent the text to be summarized. Then, we repeatedly find paths through this graph to produce concise summaries. We consider Opinosis a "shallow" abstractive summarizer as it uses the original text itself to generate summaries. This is unlike a true abstractive summarizer that would need a deeper level of natural language understanding.
While the evaluation is on an opinion dataset, the approach itself is general in that, it can be applied to any corpus containing high amounts of redundancies, for example, Twitter comments or user comments on blog/news articles. A very similar work to ours (published at the same time and at the same conference) is the following:
Multi-sentence compression: Finding shortest paths in word graphs
Proceedings of the 23rd International Conference on Computaional Linguistics (COLING 10). Beijing, China, August 23-27, 2010. Katja Filippova
Katja's work was evaluated on a news dataset (google news) for both English and Spanish while ours was evaluated on user reviews from various sources (English only). She studies the informativeness and grammaticality of sentences and in a similar way we evaluate these aspects by studying how close the Opinosis summaries are compared to the human composed summaries in terms of information overlap and readability (using a human assessor).