Implementation of text rank algorithms described in (Mihalcea and Tarau, 2004)
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License: MIT License
Text Rank with Python
License: MIT License
Implementation of text rank algorithms described in (Mihalcea and Tarau, 2004)
Look into storing the transistion scores for text rank instead of the denoms.
np.sum(adj / denom * ws, axis=0)
To
np.sum(trans * ws, axis=0)
We should be able to control the seed used to generate the initial ws
for reproducibility
There should be information in the read me about how text rank works and comments in the code about what part of the math each section is.
There should also be some comments about the differences between the delayed updates and updating as they go.
There should so be performance tables
The following things need to be unittested
Graph Building functions
Graph classes themselves
Text rank utilties
text rank
similarity functions
task utilities
Eventually we may need a SparseAdjacencyMatrix
graph class to handle large graphs, the AdjacencyList
might be enough to handle it though, it depends on speed.
This is low priority and should only be done when we find a graph we can't handle.
we need a pyproject.toml to manage black configuration and to be future proof as the python packing community moves to it
Right now the results are just qualitative, I want to add an evaluation script that at least can be used for keyword extraction with F1. A more complex one for summarization (rouge, etc) would also be nice
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