Graph-based text summarization

Graph-based approaches have become increasingly popular in text summarization due to their ability to capture complex relationships between textual elements. Here, we will explore two main categories of graph-based summarization: extractive and abstractive.

Extractive summarization using graph centrality

Extractive summarization involves selecting and arranging the most important sentences from the original text to form a concise summary. Graph-based methods for extractive summarization typically follow these steps:

1. Construct a graph representation of the text.
2. Apply centrality measures to identify important nodes (sentences).
3. Extract top-ranked sentences to form the summary.

Graph construction

The text is represented as a graph where nodes are sentences and edges represent similarities between sentences. Common similarity measures include the following:

• Cosine similarity of term frequency-inverse document frequency...