Update summary section

paper.md

@@ -45,22 +45,19 @@ bibliography: paper.bib
 
 # Summary
 
-Solving real-world mathematical optimization problems requires modern solvers to meet increasignly strict requirements, such as low latency, high solution quality, low energy consumption, and support for massive scalability. Neuromorphic computing is emerging as a promising paradygm for fine-grained parallel and event-driven computation, enabling orders of magnitude gains in Energy-Delay-Product on optimization workloads [@davies2021advancing]. 
-However, neuromorphic applications tipically suffers from a long development cycle, since the lack of effective abstraction frameworks requires deep knowledge of the target hardware platform and limits contributions from domain experts (e.g., operations researcher).
-`Lava Optimization` is a Python package ... algorithms and applications in the area of mathematical optimization.
+Solving real-world mathematical optimization problems  demands cutting-edge solvers that meet stringent criteria, including low latency, high solution quality, minimal energy consumption, and the ability to scale massively. 
+Neuromorphic computing is emerging as a promising paradygm for fine-grained parallel and event-driven computation, enabling orders of magnitude gains in Energy-Delay-Product for optimization workloads [@davies2021advancing] on novel hardware architectures. 
+Despite the potential, neuromorphic applications often face long development cycles due to the lack of effective abstraction frameworks. 
+This limitation necessitates in-depth knowledge of the target hardware platform, hindering contributions from domain experts like operations researchers. 
 
-The library provides 
+`Lava Optimization` is a Python package a Python package designed to facilitate the development of neuromorphic algorithms and applications in the area of mathematical optimization. 
+Specifically tailored for iterative, discrete, and distributed methods, the library abstracts the neuromorphic intricacies of the specific backends, offering an API typical of Operations Research (OR) composed of variables, constraints, and cost functions. 
+We leveraged this software infrastructure to develop solvers for continuous Quadratic Programming (QP) and Quadratic Unconstrained Binary Optimization (QUBO) problems, while the community contributed a Bayesian solver [@snyder2023neuromorphic] and the Local Competitive Algorithm (LCA) [@parpart2023implementing]. 
+This collaborative effort showcases the potential of `Lava Optimization` in advancing neuromorphic solutions for mathematical optimization challenges.
 
-We leveraged this software infrastructure to develop solvers for continuous Quadratic Programming (QP) and Quadratic Unconstrained Binary Optimization (QUBO) problems, while the community contributed a Bayesian solver [@snyder2023neuromorphic] and the Local Competitive Algorithm (LCA) [@parpart2023implementing].
-
-- `Lava Optimization` increases productivity on developing and testing novel neuromorphic algorithms and applications
-- The library abstracts away the neuromoprhic aspect of the backend, exposing an API typical of constrained optimization (variables, constraints, cost, etc.)
-- Supports the community  in developing algorithms that are iterative, discrete, and distributed
 
 # Statement of need
 
-A Statement of need section that clearly illustrates the research purpose of the software and places it in the context of related work.
-
 - Difficulty of programmability of neuromorphic
   - SpynNaker: more suited for neuroscience, hard to use to program algorithms [@rhodes2018spynnaker]
   - NENGO [@bekolay2014nengo], NESTML [@plotnikov2016nestml], BrainScaleS supports PyNN [@davison2009pynn], Brian [@goodman2009brian]