The document describes the TextRank algorithm for keyword and sentence extraction using a graph-based ranking approach. It outlines the steps for graph construction, including defining vertices and edges based on co-occurrence and similarity relations, and discusses performance evaluation against other summarization systems. Additionally, it highlights the algorithm's implementation in industry-standard tools like Gensim and notes common patterns in supervised topic modeling algorithms.

1. TextRank: Bringing Order into
Texts
Rada Mihalcea and Paul Tarau
Presented by :
Sharath T.S
Shubhangi Tandon

2. The TextRank Algorithm
1. Identify text units that best define the task at hand,and add them as
vertices in the graph.
2. Identify relations that connect such text units, and use these relations to
draw edges between vertices in the graph. Edges can be directed or
undirected, weighted or unweighted.
3. Iterate the graph-based ranking algorithm until convergence.
4. Sort vertices based on their final score. Use the values attached to each
vertex for ranking/selection decisions.

3. The TextRank Model
G = (V, E)
V = Set of vertices , E = Set of Edges
V(in) = Set of incoming edges
V(out) = Set of outgoing edges
d = damping factor
In addition, W = set of edge weights
Note : For undirected graphs, V(in) = V(out)

4. Convergence
Convergence of 4 different kinds of graphs
with respect to directed/undirected and
weighted unweighted.

5. KeyWord Extraction
How is the graph built?
Each word(lexical unit) is a node.
A co-occurrence relation, two vertices are connected if their corresponding
lexical units co-occur within a window of maximum words, where it can be
set anywhere from 2 to 10 words.

6. Example

7. Results for Keyword Extraction

8. Sentence Extraction
Goal is to rank entire sentences, vertex = sentence.
Co-occurrence cannot be used. Why ?
We need a new relation for our edges : Similarity.
Measured as content overlap between two sentences( nodes).

9. Evaluation
Single Document Summarisation
Data : DUC (2002) , 567 news articles
Evaluation metrics :ROUGE
Compared against 15 systems , including baseline provided by DUC

10. Results
Highly Dense Graph
Output compared to human
summaries

11. Comparison - TextRank and Opinosis
Both are unsupervised graphical algorithms
Both try to identify the regions most traversed node/path in a graph(topics,
content described most about)
TextRank uses node importances(as a word and sentence) for KeyWord
extraction and summarization whereas Opinosis uses path weights across
nodes(words) to generate fine-grained summaries.

12. Observations
1. Common pattern : usage of text-unit co-occurrence as a feature in all
supervised topic modelling algorithms ( LDA, BTM, TextRank )
2. Future work : http://web.fi.uba.ar/~fbarrios/tprofesional/articulo-en.pdf
3. Industry started :Included as a module in gensim