Summarization systems often have additional evidence they can utilize in order to specify the most important topics of document(s). For example, when summarizing blogs, there are discussions or comments coming after the blog post that are good sources of information to determine which parts of the blog are critical and interesting. In scientific paper summarization, there is a considerable amount of information such as cited papers and conference information which can be leveraged to identify important sentences in the original paper. How text summarization works In general there are two types of summarization, abstractive and extractive summarization. Abstractive Summarization: Abstractive methods select words based on semantic understanding, even those words did not appear in the source documents. It aims at producing important material in a new way. They interpret and examine the text using advanced natural language techniques in order to generate a new shorter text that conveys the most critical information from the original text. It can be correlated to the way human reads a text article or blog post and then summarizes in their own word. Input document → understand context → semantics → create own summary. 2. Extractive Summarization: Extractive methods attempt to summarize articles by selecting a subset of words that retain the most important points. This approach weights the important part of sentences and uses the same to form the summary. Different algorithm and techniques are used to define weights for the sentences and further rank them based on importance and similarity among each other. Input document → sentences similarity → weight sentences → select sentences with higher rank. The limited study is available for abstractive summarization as it requires a deeper understanding of the text as compared to the extractive approach. Purely extractive summaries often times give better results compared to automatic abstractive summaries. This is because of the fact that abstractive summarization methods cope with problems such as semantic representation, inference and natural language generation which is relatively harder than data-driven approaches such as sentence extraction. There are many techniques available to generate extractive summarization. To keep it simple, I will be using an unsupervised learning approach to find the sentences similarity and rank them. One benefit of this will be, you don’t need to train and build a model prior start using it for your project. It’s good to understand Cosine similarity to make the best use of code you are going to see. Cosine similarity is a measure of similarity between two non-zero vectors of an inner product space that measures the cosine of the angle between them. Since we will be representing our sentences as the bunch of vectors, we can use it to find the similarity among sentences. Its measures cosine of the angle between vectors. Angle will be 0 if sentences are similar. All good till now..? Hope so :) Next, Below is our code flow to generate summarize text:- Input article → split into sentences → remove stop words → build a similarity matrix → generate rank based on matrix → pick top N sentences for summary.

Semantic Flow Language (SFL) is a framework that aligns human intent, AI reasoning, and executable logic. It ensures bidirectional synchronization between meaning and code, allowing for reliable, transparent, and verifiable AI-driven development across various environments.

Official implementation code for "A Spatial Semantic Reasoning Flow for Dense Vision-Language Inference"

Node-RED Semantic Flow Language - AI-powered tooltips and natural language flow builder plugins

A Framework for Semantic Flow in Language Networks

Compiler for Tiger language, written in Standard ML for Duke ECE/CS 553: Compiler Construction. The compiler follows the standard flow of lexing, parsing, semantic analysis and type checking, intermediate representation, liveness analysis, and code generation.

Code cloning detection based on intermediate language is studied. From the perspective of code semantic information, a code cloning detection framework based on clustering algorithm is proposed. 1. Based on LLVM framework, compile the source code into bitcode files of intermediate languages; 2. Automatic extraction of control flow diagram (CFG) from bit-code files to obtain functional control flow information based on intermediate language; 3. Traverse the control flow graph based on depth-first traversal algorithm, obtain the node information of the control flow graph and combine it into a linear chain according to depth-first traversal order; 4. Perform embedding and build a clustering model for linear chains, conduct feature learning of all linear chains transformed into mathematical vectors, summarize data characteristics and use them as basis for cloning detection.

A hpc LLVM Pass extracting semantic Control-Data Flow Graphs (CDFG) from Intermediate Representation for Graph Neural Networks. Enables cross-language code retrieval and clone detection beyond token-based approaches.

Transpiler from a custom object-oriented language (ooSimple) to C, built with ANTLR4. Supports classes, multiple inheritance, methods, variables, and control flow. Implements parsing, semantic analysis with symbol tables, and structured source-to-source C code generation.

Semantic user input flow tracer - taint analysis for 11 languages

Centralized CI/CD workflows with semantic versioning, multi-language support, and GitOps promotion flows

Lexical and semantic analysis, compiling into intermediate code, language enhancements (flow-control commands, code blocks, primitive data types, relationship and boolean logic operators), assembly output, functions, and compile time optimizations.

# TinyLang Compiler Full-stack compiler in Python: lexer → parser → semantic analyzer → TAC optimizer → bytecode VM. Implements a C-like language with type checking, control flow, and three optimization passes. Educational compiler construction project with complete test suite.

A lightweight compiler for a custom programming language inspired by Go. Supports variable declarations, functions, control flow, arrays, and constant handling. Written in Python, this compiler performs lexical, syntactic, and semantic analysis, along with intermediate code generation.

This repository provides a modular framework for interacting with multiple language model APIs (OpenAI, Anthropic, and Mistral). It enables text generation, embedding retrieval, semantic search, and multi-step prompt flows, allowing users to leverage various models in a structured workflow.

This repository provides a modular framework for interacting with multiple language model APIs (OpenAI, Anthropic, and Mistral). It enables text generation, embedding retrieval, semantic search, and multi-step prompt flows, allowing users to leverage various models in a structured workflow.

A six-stage compiler pipeline in a single HTML file. Demonstrates Lexical, Syntax, Semantic, and Intermediate Code Generation (ICG) phases for a C-like language. Includes full support for arithmetic and if/else control flow. Perfect for learning compiler design.

Mini compiler in C++ for a custom language with unique syntax (e.g., === for assignment). It includes lexical analysis, LL(1) parsing, symbol table creation, and semantic analysis with type checking. Supports basic data types, control flow, variable declarations, and error handling.

This semantic layer sits between a user's natural language question and a refined SQL query ready for execution. It uses an AI-driven orchestration flow to select the right KPI set, then refine a pre-built SQL query using embedded schema context.

The Pascal Mini Compiler is a C-based implementation for a simplified version of the Pascal programming language. It comprises modules for lexical analysis, parsing, semantic analysis, code generation, interpretation, and file handling. The grammar is defined in detail, covering program structure, variables, instructions, and control flow.

The Pascal Mini Compiler is a C-based implementation for a simplified version of the Pascal programming language. It comprises modules for lexical analysis, parsing, semantic analysis, code generation, interpretation, and file handling. The grammar is defined in detail, covering program structure, variables, instructions, and control flow.

Built a PyTorch-based Vision-Language Joint-Embedding Predictive Architecture (VL-JEPA) that aligns images (ViT) and text (DistilBERT) in a shared semantic space. Implemented a Transformer predictor trained with InfoNCE loss to learn non-generative embedding prediction. Designed modular encoders, frozen targets, and validated gradient flow

‘The Unfaithful Bride' is a dramatic collection of poems that encapsulates the palpable indifference between pastor and his bride. It is imbued on the unfaithfulness of the rider's bride, who has never meant good for her lord. As a result, the poem is a metaphysical piece of art wherein the poet uses a mechanical object to articulate the phenomenon of nuptial indifferences between a couple. There is no direct relationship between a motorcycle and a bride, but through the use of metaphors, the poet illustrates a clear link between the two seemingly unrelated ideas. To his credit, the poet Fortune Nwaiwu, is able to achieve this very well. The poet is believed to have been a fan or an avid reader of George Herbert’s poems, especially that of his “Pulley”. In this collection of poems, the poet attempts to convey the pain and suffering some pastors undergo during the course of their ministerial careers. The poet-persona laments on the manner in which his motorcycle has disgraced and disillusioned him before the eyes of men. Despite spending a substantial amount of money to repair his motorcycle, the poet-persona still feels dissatisfied as he pushes the spoiled motorcycle to a far distance, sweating profusely. The poet then threatens to sell his motorcycle before it makes him go bald. Interestingly, both the pastor and his bride are bewitched. The pastor is bewitched by the beauty of his bride, which is why he paid her bride price. On the other hand, the pastor's bride was bewitched by some evil men in order to prevent the man of God from efficiently piloting his ministerial affairs. Here, it can be surmised that it is not only human beings that endure or suffer, even some humans, through inanimate primates, are not impervious to such temptations as well. The bride is seen as a wilful, wayward woman who had had a husband before her beauty enamoured the pastor. Thereafter, she is abandoned. She wanders in Aba, a commercial centre in Abia State, where she prostituted herself with lovers. This is really enshrined in the fact that when one buys a fairly used item such as a motorcycle, the machine typifying the woman is believed to have had lovers. Since KYMCO, the motorcycle could not serve her lord, which leaves him stranded on roads and heaps shame on him, she is then viewed as an unfaithful bride. However, this literary piece of art is written in a plain language, more of prosaic, although it is redolent of poetic style and forms. The poet's thoughts are very concise, which are arranged in lines and stanzas, albeit with unequal lengths. This is the reason why no unified or definite sets of rhyme schemes are observed. The rhythms are felt by repeating words that have added more beauty to the musicality of the poem. Such repetitions help the poem flow better, and enable readers to flow concomitantly to discover some semantic and metaphorical extensions encrypted in the poem. Since this poem uses metaphorical extensions, readers can read and interpret it in myriad ways, depending on their state of mind. Fortune Nwaiwu.