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nla-project's Introduction

NLA-Project

We formulate the problem of Extractive Summarization as a Problem of Regression where we assign an importance score to every sentence in the input document.

Still under maintenance, intro, results and other details to be added.

For Implementation Details on Modelling and saved Pretrained models, go here.

Data

This project uses a part of the CNN/Daily mail dataset.

  • Our labelled data can be found here
  • Pretrained embeddings on our data can be found here
  • Trained infersent and fasttext models can be found here

Code

Along with the code in the repository, you can download the colab notebooks here :

Evaluation scores

For fasttext

fscore precision recall
top 3 sentences
rouge_1 0.147817491063985 0.1191565027549791 0.21764581086768142
rouge_2 0.032386031740500275 0.025221901049095097 0.048653199042364106
rouge_l 0.12486465817585539 0.10309968285602499 0.17325227575193802
top 4 sentences
rouge_1 0.23079145635431822 0.16910484132552217 0.407750460829676
rouge_2 0.07574577683375168 0.05437105405999294 0.1374281904099026
rouge_l 0.2050603948271745 0.15447521739850095 0.33267502866279164
top 5 sentences
rouge_1 0.22238089529088254 0.15288740384962163 0.45631344462363566
rouge_2 0.07475935402793302 0.05056098905490247 0.15767976167084463
rouge_l 0.2044236926545439 0.14525485867624255 0.3750272634696634

For infersent

fscore precision recall
for top 3 sentences
rouge_1 0.23837762616307634 0.17870970342922088 0.3948210652772677
rouge_2 0.0782952265255066 0.0586625199432189 0.1290943302874871
rouge_l 0.20978198592978672 0.16219584658887584 0.32059361189593455
top 4 sentences
rouge_1 0.2263082742007271 0.15504706773578525 0.4615344756485788
rouge_2 0.07889476867782748 0.05397341665789107 0.16101318569672227
rouge_l 0.2087232440138955 0.14863005286219036 0.3777621191330641
top 5 sentences
rouge_1 0.21218549949989918 0.13743780821078694 0.514433460338619
rouge_2 0.07780463190895977 0.05039379155868287 0.18894813470729832
rouge_l 0.20578917643633968 0.13901717381078943 0.427076310748189

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