Implement IR based Supervised Sentence Ranker

[dafajon]

• This PR includes a retrieval based supervised summarizer implemented using lightgbm ranker.
• sadedegel.dataset.annotated is used with its sentence, relevance pairs to train ranker.
• Evaluation is done by leave-one-out cross validation due to small number of documents (~100).
• Optimization with optuna is also implemented for user specified summarization length or picked embedding type.

test_supervised.py

• Implement test for initializing ranker with lazy loading of the appropriate model.
• Test re-loading of model when embedding type is switched.
• Test for summary output with specified sentence length.

supervised.py

• Implement SupervisedSentenceRankerclass as child of ExtractiveSummarizer.
• Embedding generation phase prepares string input to doc-sentence representation for the LGBMRanker. Decouple embedding generation for transformer based and BoW based representations from predict method.
• Implement a tuner class as RankerOptimizer if the user requires an optimized ranker for a summarization_percentage and another embedding with vector_type. Inherit SupervisedSentenceRanker for its embedding extraction methods.
• _prepare_dataset uses extraction methods to prepare dataset for the format required for LGBMRanker.

util/supervised_tuning.py

• Implement components for optimization of ranker.
• Implement Logging and parsing for parameters of the best trial.
• Implement Objective function for optuna with sampling of parameter space.
• Implement Callback for Live status update rather that verbosity of optuna.
• Implement fitting and saving for model with best hyperparameters.

README.md

• Update with usage of all summarizers.
• Add usage of supervised sentence ranker and tuner.
• Add scores for the ranker.

model/ranker_bert_128k_cased.joblib

• Add default model for the ranker.
• User trained custom rankers via RankerOptimizer are serialized to ~/.sadedegel_data/models

[askarbozcan]

ToDo: More data for supervised ranker summarizers.