Q$6$: Multi$-$document summarization

a$.$ We want to merge 'paragraph$-1\text{'}$ and 'paragraph$-2\text{'}.$ Work on TFIDF based approach for multi$-$document summarization for 'paragraph$-1\text{'}$ and 'paragraph$-2\text{'}.$

b$.$ Perform POS tagging on 'paragraph$-2\text{'}$ also. Explain in detail. Discuss NLTK in this regard.

c$.$ How can you perform abstractive summarization here. Explain two approaches

Paragrah$-1$

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Affective computing is an interdisciplinary umbrella that comprises systems that recognize,

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interpret, process or simulate human feeling, emotion and mood. For example, some virtual assistants are programmed to speak conversationally or even to banter humorously; it makes them appear more sensitive to the emotional dynamics of human interaction, or to otherwise facilitate human$-$computer interaction. However, this tends to give na$$ve users an unrealistic conception of how intelligent existing computer agents actually are. Moderate successes related to affective computing include textual sentiment analysis and, more recently, multimodal sentiment analysis, wherein AI classifies the affects displayed by a videotaped subject

Paragrah$-2$

The simplest AI applications can be divided into two types: classifiers $($e$.$g$."$if shiny then diamond"$),$ on one hand, and controllers $($e$.$g$."$if diamond then pick up$"),$ on the other hand. Classifiers are functions that use pattern matching to determine the closest match. They can be fine$-$tuned based on chosen examples using supervised learning. Each pattern $($also called an "observation"$)$ is labeled with a certain predefined class. All the observations combined with their class labels are known as a data set. When a new observation is received, that observation is classified based on previous experience.

Q$7$: Text Pre$-$processing and topic modeling:

a$.$ Perform different pre$-$processing tasks on 'paragraph$-1\text{'}$ and 'paragraph$-2\text{'}.$ Write pseudo code and produce sample output.

i$.$ Remove Punctuations

ii$.$ Converting into Text Tokens $($Tokenization$)$

iii. Remove Stop words

iv$.$ Normalize the data

v$.$ Lemmatization

vi$.$ Feature Extraction

vii. Using BoW

viii. Count N$-$grams $($Bigrams $\backslash$& Trigrams$)$

ix$.$ TF$-$IDF

b$.$ Perform topic modelling on 'paragraph$-1\text{'}$ and 'paragraph$-2\text{'}$

c$.$ What is LDA? How can you apply LDA here.