Remove BDA + TM

src/year2/big-data-analytics-and-text-mining/metadata.json

@@ -1,15 +0,0 @@
-{
-    "name": "Big Data Analytics and Text Mining",
-    "year": 2,
-    "semester": 1,
-    "pdfs": [
-        {
-            "name": "Text mining",
-            "path": "module1/tm.pdf"
-        },
-        {
-            "name": "Big data analytics",
-            "path": "module2/bda.pdf"
-        }
-    ]
-}

src/year2/big-data-analytics-and-text-mining/module1/ainotes.cls

@@ -1 +0,0 @@
-../../../ainotes.cls

src/year2/big-data-analytics-and-text-mining/module1/sections/_summarization.tex

@@ -1,138 +0,0 @@
-\chapter{Automatic text summarization}
-
-\begin{description}
-    \item[Extractive summarization] \marginnote{Extractive summarization}
-        Select fragments of text.
-
-    \item[Abstractive summarization] \marginnote{Abstractive summarization}
-        Rephrase the content of the text.
-
-    \item[Hybrid summarization] \marginnote{Hybrid summarization}
-        Apply an extractive method followed by an abstractive one.
-\end{description}
-
-
-\begin{description}
-    \item[Generic vs query-focused] \phantom{}
-        \begin{description}
-            \item[Generic]
-                Summary of the whole document.
-            \item[Query-focused]
-                Summary that replies to given questions
-        \end{description}
-
-    \item[Technical vs lay] \phantom{}
-        \begin{description}
-            \item[Technical]
-                Summary using scientific language.
-            \item[Lay]
-                Summary using common language.
-        \end{description}
-
-    \item[Narrative vs bullet point] \phantom{}
-        \begin{description}
-            \item[Narrative]
-                Standard textual summary.
-            \item[Bullet point]
-                Set of key phrases.
-        \end{description}
-
-    \item[Single document vs multi document] \phantom{}
-        \begin{description}
-            \item[Single document]
-                Summary covering a single document.
-            \item[Multi document]
-                Summary covering multiple documents.
-        \end{description}
-
-    \item[Short document vs long document] \phantom{}
-        \begin{description}
-            \item[Short document]
-                Summary of a document with a few tokens.
-            \item[Long document]
-                Summary of a document with many tokens.
-        \end{description}
-\end{description}
-
-
-
-\section{Metrics}
-
-Summarization metrics can evaluate different levels:
-\begin{descriptionlist}
-    \item[Syntactic] 
-        Check word overlapping (e.g., ROUGE). 
-    \item[Semantic] 
-        Check semantic coverage (e.g., BERTScore). 
-    \item[Factuality] 
-        Check factuality to the source (e.g., BARTScore).
-    \item[Fluency] 
-        Check for redundancies (e.g., unique N-gram ratio).
-    \item[Efficiency] 
-        Measure trade-off between performance and costs (e.g., CARBURACY). 
-\end{descriptionlist}
-
-
-\subsection{Recall-Oriented Understudy for Gisting Evaluation (ROUGE)}
-
-\begin{description}
-    \item[ROUGE] \marginnote{ROUGE}
-        N-gram oriented metric that compares the generated summary and the ground truth.
-
-        \begin{description}
-            \item[ROUGE-1] Overlap of 1-grams.
-            \item[ROUGE-2] Overlap of 2-grams.
-            \item[ROUGE-L] Length of the common longest subsequence.
-        \end{description}
-\end{description}
-
-
-\begin{description}
-    \item[Precision] 
-        \[ \texttt{ROUGE}_\texttt{precision} = \frac{\vert \text{overlaps} \vert}{\vert \text{generated summary} \vert} \]
-
-    \item[Recall] 
-        \[ \texttt{ROUGE}_\texttt{recall} = \frac{\vert \text{overlaps} \vert}{\vert \text{ground truth} \vert} \]
-\end{description}
-
-
-\subsection{Limitations}
-
-\begin{itemize}
-    \item ROUGE only evaluates on a syntactic level.
-    \item ROUGE-2 and ROUGE-L are sensitive to the position of words.
-\end{itemize}
-
-
-
-\section{State-of-the-art generative summarizers}
-
-\subsection{BART}
-
-\begin{itemize}
-    \item \marginnote{BART} 
-        Encoder-decoder Transformer with an input size of 1024 tokens.
-    \item It is suited for short document summarization.
-    \item It is pre-trained using a denoising sequence-to-sequence approach.
-\end{itemize}
-
-
-\subsection{Longformer encoder-decoder} 
-
-\begin{itemize}
-    \item \marginnote{Longformer encoder-decoder}
-        Encoder-decoder Transformer with an input size of 16k tokens.
-    \item It is suited for long document summarization.
-    \item It uses a linear encoder self-attention based on global and local attention that reduces the quadratic complexity of the standard attention mechanism.
-\end{itemize}
-
-
-\subsection{PRIMERA}
-
-\begin{itemize}
-    \item \marginnote{PRIMERA}
-        Encoder-decoder Transformer based on Longformer with an input size of 4K tokens.
-    \item It is suited for long document summarization.
-    \item It has an ad-hoc pre-training for multi document summarization.
-\end{itemize}
-

src/year2/big-data-analytics-and-text-mining/module1/tm.tex

@@ -1,13 +0,0 @@
-\documentclass[11pt]{ainotes}
-
-\title{Big Data Analytics and Text Mining\\(Module 1)}
-\date{2024 -- 2025}
-\def\lastupdate{{PLACEHOLDER-LAST-UPDATE}}
-\def\giturl{{PLACEHOLDER-GIT-URL}}
-
-\begin{document}
-
-    \makenotesfront
-    \input{./sections/_summarization.tex}
-
-\end{document}

src/year2/big-data-analytics-and-text-mining/module2/ainotes.cls

@@ -1 +0,0 @@
-../../../ainotes.cls

src/year2/big-data-analytics-and-text-mining/module2/bda.tex

@@ -1,12 +0,0 @@
-\documentclass[11pt]{ainotes}
-
-\title{Big Data Analytics and Text Mining\\(Module 2)}
-\date{2024 -- 2025}
-\def\lastupdate{{PLACEHOLDER-LAST-UPDATE}}
-\def\giturl{{PLACEHOLDER-GIT-URL}}
-
-\begin{document}
-
-    \makenotesfront
-
-\end{document}